{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Petroleum Data Analytics Projects.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPaRsNN2t4uSr/yxGcXZ5cQ"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "8E1O1ecYtkaB"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Po3DnqbPutFD"
      },
      "source": [
        "# 1. Calculating Res-Permeability using Data : Well Test Analysis\n",
        "\n",
        "> NumPy | Plotting | Domain Concepts | Reservoir-Engineering\n",
        "\n",
        "### Pwf = Pi - m[log(t) + C] "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7MoLzbyOuM1U"
      },
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/Divyanshu-ISM/Petroleum-DS-ML-with-Python/main/Pt.csv')"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "Pmci_15LFbgR",
        "outputId": "9e8e4efd-b90b-4295-d9e1-45fd602413a8"
      },
      "source": [
        "df"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>pressure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>2743.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0021</td>\n",
              "      <td>2737.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0034</td>\n",
              "      <td>2730.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0048</td>\n",
              "      <td>2722.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0064</td>\n",
              "      <td>2713.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>48.7900</td>\n",
              "      <td>2269.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>54.7900</td>\n",
              "      <td>2267.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>60.7900</td>\n",
              "      <td>2265.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>66.7900</td>\n",
              "      <td>2264.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>72.0000</td>\n",
              "      <td>2262.99</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>78 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       time  pressure\n",
              "0    0.0010   2743.96\n",
              "1    0.0021   2737.52\n",
              "2    0.0034   2730.16\n",
              "3    0.0048   2722.32\n",
              "4    0.0064   2713.83\n",
              "..      ...       ...\n",
              "73  48.7900   2269.77\n",
              "74  54.7900   2267.69\n",
              "75  60.7900   2265.73\n",
              "76  66.7900   2264.46\n",
              "77  72.0000   2262.99\n",
              "\n",
              "[78 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9Abq8gix9CD"
      },
      "source": [
        "### A. Cartesian Analysis : Tells what kind of test is it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "HLSzkYDewIcq",
        "outputId": "6e66d08a-689b-47be-9b7e-503ac31a0535"
      },
      "source": [
        "plt.style.use('default')\n",
        "plt.figure(figsize=(12,4))\n",
        "\n",
        "plt.plot(df['time'], df['pressure'],lw=3)\n",
        "\n",
        "plt.xlabel('Time') ; plt.ylabel('Pressure')\n",
        "\n",
        "plt.title('Cartesian Plot : Drawdown Testing')\n",
        "plt.grid()\n"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PiNxSWLVyBsb"
      },
      "source": [
        "### B. Semilog Analysis : High level searching for MTR."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "Ih9BUpRIyHk7",
        "outputId": "8256aa61-239f-41b3-a206-c73ce1487914"
      },
      "source": [
        "plt.style.use('default')\n",
        "plt.figure(figsize=(12,4))\n",
        "\n",
        "plt.semilogx(df['time'], df['pressure'],lw=3)\n",
        "\n",
        "plt.xlabel('Log-Time') ; plt.ylabel('Pressure')\n",
        "\n",
        "plt.title('Semilog Plot : Drawdown Testing')\n",
        "plt.grid()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AOfAZU9myUjv"
      },
      "source": [
        "### C. Log-Log Plot : Computations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "M0--g7C0wJkq",
        "outputId": "6b20ee97-1f01-48e5-b4d3-b8b1e42edaf8"
      },
      "source": [
        "Pi = 2760 #psi Given\n",
        "\n",
        "df['delP'] = np.abs(Pi - df['pressure'])\n",
        "\n",
        "#note that np.diff(P) does what we call as 'dP' in 'dP/dt'. \n",
        "#But it reduces one element.\n",
        "df['d(delP)'] = np.zeros(len(df))\n",
        "df['d(delP)'].iloc[1:] = np.abs(np.diff(df['delP']))\n",
        "\n",
        "\n",
        "df['dt'] = np.zeros(len(df))\n",
        "df['dt'].iloc[1:] = np.diff(df['time'])\n",
        "\n",
        "\n",
        "#P' (Pdash) = t*dP/dt\n",
        "df['Pdash'] = np.zeros(len(df))\n",
        "df['Pdash'].iloc[1:] = df['time']*df['d(delP)']/df['dt']\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>pressure</th>\n",
              "      <th>delP</th>\n",
              "      <th>d(delP)</th>\n",
              "      <th>dt</th>\n",
              "      <th>Pdash</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0010</td>\n",
              "      <td>2743.96</td>\n",
              "      <td>16.04</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0021</td>\n",
              "      <td>2737.52</td>\n",
              "      <td>22.48</td>\n",
              "      <td>6.44</td>\n",
              "      <td>0.0011</td>\n",
              "      <td>12.294545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0034</td>\n",
              "      <td>2730.16</td>\n",
              "      <td>29.84</td>\n",
              "      <td>7.36</td>\n",
              "      <td>0.0013</td>\n",
              "      <td>19.249231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0048</td>\n",
              "      <td>2722.32</td>\n",
              "      <td>37.68</td>\n",
              "      <td>7.84</td>\n",
              "      <td>0.0014</td>\n",
              "      <td>26.880000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0064</td>\n",
              "      <td>2713.83</td>\n",
              "      <td>46.17</td>\n",
              "      <td>8.49</td>\n",
              "      <td>0.0016</td>\n",
              "      <td>33.960000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     time  pressure   delP  d(delP)      dt      Pdash\n",
              "0  0.0010   2743.96  16.04     0.00  0.0000   0.000000\n",
              "1  0.0021   2737.52  22.48     6.44  0.0011  12.294545\n",
              "2  0.0034   2730.16  29.84     7.36  0.0013  19.249231\n",
              "3  0.0048   2722.32  37.68     7.84  0.0014  26.880000\n",
              "4  0.0064   2713.83  46.17     8.49  0.0016  33.960000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "9MsScgaVwug5",
        "outputId": "5e86c95b-4005-484c-bd13-5b2f7ad3e64f"
      },
      "source": [
        "plt.style.use('default')\n",
        "plt.figure(figsize=(12,4))\n",
        "\n",
        "plt.loglog(df['time'], df['Pdash'],'o',label='Derivative, Pdash')\n",
        "plt.loglog(df['time'], df['delP'],'o',label='Differential Pr, Del(P)')\n",
        "\n",
        "plt.axvspan(1,100,color='green',alpha=0.2,label='IARF')\n",
        "\n",
        "plt.xlabel('Time(Log Axis)') ; plt.ylabel('Pressure(Log Axis)')\n",
        "\n",
        "plt.title('Log-Log/Derivative/Diagnostic Plot : Drawdown Testing')\n",
        "\n",
        "\n",
        "plt.legend()\n",
        "plt.grid(True, which=\"both\")"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WmpquQBH50UbPnj1hb2+vtFY+Ozsbx48fx6hRo/g0oVDIR+BlMhmysrJQVlaGDh068FNNK8srr7yCtm3bKtUtlUqxe/duDBo0iF8+sGvXLlhZWaFPnz5KfW/fvj3EYrFS33NycnD27Fm9psOXp+L1Vkf55QxPnjzB48eP0bVrVzDGcPnyZQBAWloarl27hrFjxypF7QIDA9G6dWu1fkeNGgUbGxv+vWKTrX/++QeAfMPCK1euICwsDLa2trxdmzZt0KdPH37cAoC1tTXOnTuHtLQ0vfuuiUePHiE6Ohpvv/02GjVqpJSn7/F88+fPh4ODA5ycnNC9e3ckJCTgq6++4mdfVKQy3wmVxcPDA4wxg0xN1jReTExM1C4xKT928vPz8fjxY/j7+6OwsBC3b98GIN83oWvXroiOjgYA3Lp1C5mZmfjss8/AGMPZs2cByKPdiig4IP9+EYlESve7UChU+T6qzDjy9/fHpUuX8OTJEwDyWS8DBgxA27Zt+Wh/TEwMOI5Dt27dlOrx8vJS2ijOwcEBzZs358dzVbhy5Qr+/vtvjBkzBpmZmfx3wJMnT9CrVy9ER0fzm2ka8h4oz3vvvaf03t/fX6lPR48ehZGREd59910+TSAQYMqUKQZtB0EQ9RMS7ARBvJA8evQIhYWFaN68uUpey5YtIZPJkJKSAkC+OVjTpk1V7Cqm5ebmIiMjg39lZWXp3Z7k5GQIBAIVn05OTrC2tkZycjJvp65uW1tbJeGl4NChQ5UWkAUFBQAAS0tLAMDff/8NQC6wHRwclF7Hjh3Dw4cPlcqLRCK4urrqrEckEmH48OHYt28fv04/IiICpaWlSoIdADZv3ow2bdrA1NQUdnZ2cHBwwKFDh5Cbm1upvpVn1KhROH36NFJTUwHI144/fPhQqe6///4bubm5cHR0VOl7QUGBUt//+OMPAEDfvn0r1Y6K11sd9+/f58WOYh1rYGAgAPDXQNPY0JQGQEUMK8aQYn2+wqem+0QhXADg66+/xvXr1+Hm5oZOnTohPDy8ykJJUa5Vq1ZVKg8AEyZMwPHjxxEZGYmLFy/i4cOHmDlzpkb7ynwn1CWaxkvDhg3Vnixw48YNDBs2DFZWVpBIJHBwcOA3Uyt///j7++PixYsoKipCTEwMnJ2d4evrCx8fH14ox8bGKgni5ORkODs7q0zrrngNKzOO/P39UVZWhrNnz+LOnTt4+PAh/P39ERAQoCTYvby8lMQ/oDqeAfmYVrffhL4ovv9CQ0NVvgPWrVuH4uJi/joa8h5QYGpqyu/PoaBinxSfQ8XlR5rue4IgXixoDTtBEISefPDBB9i8eTP/PjAwsNJngusbPdSHxMRE3L59G6tXr65UuevXrwP478eeInq0detWpfXeCiruvm5iYgKBQL/nvW+88QbWrFmDI0eOYOjQofjtt9/QokULfsd0ANi2bRvCwsIwdOhQfPLJJ3B0dIRQKMSSJUv4dclVYdSoUZg1axZ27dqFDz/8EL/99husrKzQr18/3kYmk8HR0RHbt29X66P8D+nDhw/Dz88PVlZWlWrH9evX4ejoqPEYPalUij59+iArKwuffvopWrRoAQsLC6SmpiIsLKxaR+UJhUK16YyxSvsaOXIk/P398fvvv+PYsWP45ptv8NVXXyEiIgL9+/evchurSrNmzZSO63pRqHh/KlC3qWROTg4CAwMhkUiwcOFCeHp6wtTUFJcuXcKnn36qNHa6deuG0tJSnD17FjExMbww9/f3R0xMDG7fvo1Hjx4pCfaaoEOHDjA1NUV0dDQaNWoER0dHvPLKK/D398ePP/6I4uJixMTEYNiwYSplDTmeFSiu0TfffKPxOEDFA4uauAc09YkgCEIBCXaCIF5IHBwcYG5ujjt37qjk3b59GwKBgD/n2N3dHffu3VOxq5g2c+ZMpWOA1EW8NeHu7g6ZTIa///5bafOqBw8eICcnB+7u7rydou7GjRvzdpmZmSpRpEOHDsHKykpl2qgutm7dCo7j+A2tFOcPOzo6GlwABQQEwNnZGTt37kS3bt1w8uRJlV3Ad+/ejSZNmiAiIkLpgcb8+fOV7Cr7sKNx48bo1KkTdu7cialTpyIiIgJDhw5V2kzQ09MTJ06cgJ+fn9Zd9hljOHr0KL+zvb6cPXsWCQkJWo+PunbtGu7evYvNmzdj7NixfHrFjerKj42KqEvTB4VPTfeJvb09LCws+DRnZ2dMnjwZkydPxsOHD+Hr64tFixbxYkXfz0gxJV0hTmuDynwnGPLBWmXZunUrACAoKEinbVRUFDIzMxEREcFvFAeo3ym/U6dOMDY2RkxMDGJiYvDJJ58AkN+jP//8MyIjI/n3Ctzd3REZGYmCggKlKHvFa1iZcWRsbIxOnTohJiYGjRo1UnpwUFxcjO3bt+PBgwdK7ahJFN9/EolEr+8/Q90DlcHd3R2nTp1SOcqvqvc9QRDPFzQlniCIFxKhUIi+ffti3759SseyPXjwADt27EC3bt34iGdQUBDOnj2LK1eu8HZZWVkqUVcvLy/07t2bf7Vv317v9gwYMAAAsHz5cqX0//3vfwDAT2vv1asXRCKRStR81apVKj4PHz6Mvn37Vur88aVLl+LYsWMYNWoUmjVrBkDef4lEgsWLF6O0tFSlzKNHj/T2XxGBQIARI0bgwIED2Lp1K8rKylSmwysiTOWjZOfOnePX1SpQ/FDVdKSUOkaNGoW//voLGzZswOPHj1XqHjlyJKRSKb744guVsmVlZXxdcXFxePjwYaWWHyQnJyMsLAzGxsa8OFKHuv4zxvD9998r2bm4uKBVq1bYsmULP20aAP78809cu3ZN73aVx9nZGW3btsXmzZuVruv169dx7NgxftxKpVKV5QmOjo5wcXFROpbQwsJCr2UMDg4OCAgIwIYNG3D//n2lvOpES7VRme8EhbjUd6wZ6li3HTt2YN26dejSpQt69eql017d2CkpKcGPP/6oYmtqaoqOHTvil19+wf3795WEclFREVasWAFPT084OzvzZQYMGICysjKl7yOpVIqVK1cq+dZ3HCnw9/fHuXPncOrUKb4d9vb2aNmyJb766ivepjZo3749PD09sWzZMqX7SoHi+8/Q90BlCAoKQmlpKX7++Wc+TSaT4YcffjBoPQRB1E8owk4QxHPNhg0bcPToUZX0Dz74AF9++SWOHz+Obt26YfLkyRCJRFizZg2Ki4uVzsWeOXMmtm3bhj59+mDatGn8sW6NGjVCVlaW3hGTe/fu4csvv1RJb9euHYKDgxEaGoq1a9fy01jPnz+PzZs3Y+jQofyZ4Q0aNMAHH3yAb7/9FoMHD0a/fv0QHx+PI0eOwN7enm9LUVERTp06hZ9++kltW8rKyrBt2zYAwNOnT5GcnIz9+/fj6tWr6NGjB9auXcvbSiQSrF69Gm+99RZ8fX3xxhtvwMHBAffv38ehQ4fg5+en9oGBvowaNQorV67E/Pnz0bp1a5XjsQYOHIiIiAgMGzYMwcHBSExMxE8//QQvLy+lH9BmZmbw8vLCzp078corr8DW1hatWrXSug565MiRmDFjBmbMmAFbW1uVCFpgYCAmTpyIJUuW4MqVK+jbty+MjIzw999/Y9euXfj+++8xYsQIHDp0CB4eHvDy8lJbz6VLl7Bt2zbIZDLk5OQgLi4Oe/bsAcdx2Lp1q8bj5wCgRYsW8PT0xIwZM5CamgqJRII9e/aoXZe7ePFiDBkyBH5+fhg3bhyys7OxatUqtGrVSq3Y0IdvvvkG/fv3R5cuXTB+/Hj+OC4rKyv+nPv8/Hy4urpixIgR8PHxgVgsxokTJxAXF4dvv/2W99W+fXvs3LkTH3/8MTp27AixWIxBgwaprXfFihXo1q0bfH19MWHCBDRu3BhJSUk4dOiQ0sMzQ6Lvd0Lbtm0hFArx1VdfITc3FyYmJujZsyccHR3V+q3KsW67d++GWCxGSUkJUlNT8ccff+D06dPw8fFROspPG127doWNjQ1CQ0Px/vvv8+NN00MPf39/LF26FFZWVvxGhY6OjmjevDnu3LmDsLAwJftBgwbBz88Pn332GZKSkuDl5YWIiAi1glSfcVS+HYsWLUJKSoqSMA8ICMCaNWvg4eGh1z4ZhkAgEGDdunXo378/vL29MW7cODRs2BCpqak4deoUJBIJDhw4UCP3gL4MHToUnTp1wvTp03Hv3j20aNEC+/fv5/dRqcsZIQRB1AJ1szk9QRBE9VAc66bplZKSwhiTHw8UFBTExGIxMzc3Zz169GBnzpxR8Xf58mXm7+/PTExMmKurK1uyZAlbsWIFA8AyMjJ0tkdx5JC61/jx4xljjJWWlrIFCxawxo0bMyMjI+bm5sZmzZrFH72koKysjM2dO5c5OTkxMzMz1rNnT3br1i1mZ2fH3nvvPcYYYwcPHmQcx7EHDx6otEVxVJDiZW5uzjw8PNjw4cPZ7t27mVQqVduHU6dOsaCgIGZlZcVMTU2Zp6cnCwsLYxcuXFDyrenIuIrHuimQyWTMzc2NAWBffvml2vzFixczd3d3ZmJiwtq1a8cOHjyo1t+ZM2dY+/btmbGxsdKRXRWPdSuPn5+f2iP1yrN27VrWvn17ZmZmxiwtLVnr1q3ZzJkzWVpaGmOMsQ4dOrDJkyerlFMc1aR4iUQiZmtryzp37sxmzZqlcmQZY+qPdbt58ybr3bs3E4vFzN7enr377rssPj5e7ZFNv/76K2vRogUzMTFhrVq1Yvv372fDhw9nLVq0UGmXuuPmyl83BSdOnGB+fn7MzMyMSSQSNmjQIHbz5k0+v7i4mH3yySfMx8eHWVpaMgsLC+bj48N+/PFHJT8FBQVszJgxzNraWumoOXVHWjHG2PXr19mwYcOYtbU1MzU1Zc2bN2dz585VaXN59DlKT1tf9f1O+Pnnn1mTJk2YUCjUecRbVY51U7xMTU2Zq6srGzhwINuwYYPK9wFj8mPdvL291fo7ffo0e/XVV5mZmRlzcXFhM2fO5I9UrNjmQ4cOMQCsf//+SunvvPMOA8DWr1+v4j8zM5O99dZbTCKRMCsrK/bWW2/xR0NW/Dx1jSMFeXl5TCgUMktLS1ZWVsanb9u2jQFgb731lkoZTUdnBgYGssDAQLXXRh0Vj3VTcPnyZfbaa68xOzs7ZmJiwtzd3dnIkSNZZGQkY6xm7gFN36fqvs8ePXrExowZwywtLZmVlRULCwtjp0+fZgDYr7/+qnf/CYJ4/uAYq6G5ZwRBEM85H374IdasWYOCgoI63xgoJycHNjY2+PLLLzFnzhxMnjwZFy5cwPnz5+u0XS8DDx48gLOzMw4ePKgytbe+0LZtWzg4OKiseycI4sVl7969GDZsGGJjY+Hn51fXzSEIooagNewEQRCQTzEvT2ZmJrZu3Ypu3brVuliv2Bbgv7Xv3bt3ByAXaAsWLKjNZr205ObmYt68efyyhbqktLQUZWVlSmlRUVGIj4/nxwZBEC8eFf8uKPYSkEgk8PX1raNWEQRRG1CEnSAIAnIB3L17d7Rs2RIPHjzA+vXrkZaWhsjIyFrbrVjBpk2bsGnTJgwYMABisRixsbH45Zdf0LdvX/4scOLlJCkpCb1798abb74JFxcX3L59Gz/99BOsrKxw/fp12NnZ1XUTCYKoAd555x0UFRWhS5cuKC4uRkREBM6cOYPFixdj1qxZdd08giBqENp0jiAIAvLdkHfv3o21a9eC4zj4+vpi/fr1tS7WAaBNmzYQiUT4+uuvkZeXx29Ep25DO+LlwsbGBu3bt8e6devw6NEjWFhYIDg4GEuXLiWxThAvMD179sS3336LgwcP4unTp2jatClWrlyJqVOn1nXTCIKoYSjCThAEQRAEQRAEQRD1EFrDThAEQRAEQRAEQRD1EBLsBEEQBEEQBEEQBFEPeanXsMtkMqSlpcHS0hIcx9V1cwiCIAiCIAiCIIgXHMYY8vPz4eLiAoFAewz9pRbsaWlpcHNzq+tmEARBEARBEARBEC8ZKSkpcHV11WrzUgt2S0tLAPILJZFIAMjPuD127Bj69u0LIyMjreX1tdXHTpdNZdpVn6mrftREvYbwWRUfNEZrnrroy8s6RvW1pzGqzIsyRg3h90Uao3mFeYg5FQOLFhYwNd544TUAACAASURBVDbVqy/1FZlUhuwb2bDxtoFAWHsrMGui3ur6rEr5ypbR116XXXXznydojFa/fGXK1dYYfVryFE9uP4F/D39IzCV696W2ycvLg5ubG69HtfFSC3bFNHiJRKIk2M3NzSGRSPQSQ/rY6mOny6Yy7arP1FU/aqJeQ/isig8aozVPXfTlZR2j+trTGFXmRRmjhvD7Io1RiABzc3PYWNvA3MRcr77UV2RSGYrNi2FnY1frYsjQ9VbXZ1XKV7aMvva67Kqb/zxBY7T65StTrrbGaGFxIZg5k+u7eizYFeizLPv5vtMIgiAIgiAIgiAI4gWFBDtBEARBEARBEARB1ENIsBMEQRAEQRAEQRBEPeSlXsOuL1KpFKWlpSrppaWlEIlEePr0KaRSqcby+tjpstG3rvpOXfWjJuo1hM+q+KhMGUOOUTr6kCAIgiAIgiBqFxLsWmCMISMjAzk5ORrznZyckJKSolXM6GOny0bfuuo7ddWPmqjXED6r4qMyZQw5RjmO03lOJEEQBEEQBEEQhoMEuxYUYt3R0RHm5uYqQkYmk6GgoABisVirkNHHTpeNvnXVd+qqHzVRryF8VsVHZcoYaozKZDKkpqbC2toajDG92kkQBEEQBEEQRPUgwa4BqVTKi3U7Ozu1NjKZDCUlJTA1NdUphnTZ6bLRt676Tl31oybqNYTPqvioTBlDjlEHBwfk5uY+10syCIIgCIIgiBpGJoU44xqMCjNRam6HAqfWADiAyWCZfgXGT7P/SxcI67q19R4S7BooKysDID8PlSAIwMjICBzHkWAnCIIgCIKo76gTzeXFcbn8ElMbgNlXzodMCsv0eDTMSoA43RNPXHwAgRDWidFwO/MDjJ884t2UWDggs0kPtLp9AmZXspTSU7pOQU7jgJq6Ci8EJNg1oJj2+zyvFycIQ6K4F2hKPEEQBEEQRBWRSWGXfwu2CbdRJrbXKqQ1RqEr2OQ5eCtlaxLNCnGsLt/dyBapdtOQ6xmo0wcA5bxkeV6WZw80uPqbSpeNnjyC0zX16U2Oh+OfPuEk2rVAgp0gCIIgCIIgiJebSkSk9RXSFW2sE6PhemYVTJ48Bu7J03QJ6YpRaHU2xRb2YI6jAQyBdWI0mhwPV+meQhw/aDNSrag2Lc2CZ+QC/COQl9XmQx1GTx7xfiuGOzkATEu625kfkOPuR9PjNUCCHfIjrRTHtin+LSsrA2MMMpkMMplMbTlFpFFhpwl97HTZ6FtXbdGzZ0/4+Pjgu+++q1S5yvZjwYIF2LdvHy5dulSldla13qr4rMo10dSupKQkeHp64uLFi2jbtq1eZSrjvyp2CpuysjKNxxyW//d5pi76UhN1GsJnVXxUtow+9oawoTFaP+usrt8XaYwqluMxKYNMWvd/66uDov213Y+aqLe6PqtSvrJl9LXXZVfd/BpBJoXlg2swKsxCqbkt8huoCmmt+RpsZIzj+2KTFAO3v36QC+lnFFvYI+XVKcj28NeZD0AvH00iF6h0TyGCM1qP1BqFTug1HwDU+jB+8hgdE1fi7wQruMb9BECzOG5wbbfWfNfTq5TS1NloytOGpnwOgPGTR7BIi0e+c9tqj0Em1f57tb5QmbZx7CWc3/rDDz/ghx9+gFQqxd27d7Fjxw6VteoikQhOTk5wc3ODsbFxteqTyhgupeTh8ZMS2FsYw9dNAqGg5qbaT548Gb/88gsAeT9sbGzg7e2N4cOHY8yYMQbZdC07OxsikQiWlpbV9qXAxsYG27ZtQ3BwMJ9WUFCAkpIS2NraGqwefduiwNLSEi1btsScOXMQEKB5us7AgQPRunVrLFmypNr1379/Hz4+PoiOjkbr1q2r7c8QlJSUICUlBRkZGfyPSoIgCIIgXlKYDHYFd2BamoOnRtbIFDcHOIH++XrYOOfEofW/22FW+t+65yIjW1xzDUG6dUed+fr66Ji4EoCyqFQIpHuO/dH04RGN+XGNpwGAVh9xHlPQOvUXmJZmqRWuDAADBw5MY36RyAYcB5iWZmu0KRZawlSarya3/nPBfRJSbbvUdTNqjcLCQowZMwa5ubmQSCRabV/KCPuUKVMwZcoU5OXlwcrKCn379uUvVGlpKY4fP46uXbsiPT0dYrEYpqamav0wxpCfnw9LS0uNa92PXs/AwoM3kZFXzKc5SUwxb2BL9GvlpLcvfepSYGRkhKCgIGzYsAFSqRQPHjzAH3/8gVmzZuHQoUPYt28fRKKqffQlJSUwNjbWObA0oasfZmZmSr6rWk9l61XH+vXr0a9fPzx+/Biff/453njjDVy9ehVNmjRR61MkElX62mhql1gsBgBYWFio+KtMX/S11ceuqKgIANC1a1e+feVR3Dt9+vSBkZGR1nbVd+qiLzVRpyF8VsVHZcvoY28IGxqj9bPO6vp9kcZoflE+Yk7FwMbbBmYmZnr1pb4ik8qQEZ8BJx8nCIS1eCpMDdRbXZ9VKV/ZMkr2HNMYcebtWjvA6vENFRt981ta34P7+R9rPCLteXmlSl9NS7PQMXGlPCKdqH5qd8fElXxEWpuPOI8p8Hm4E4DmaHLTR39ozfd58ItSmjqbthnbYFyaq9KO8nYcNMdQOQDmZdka8xU2z6tYBwCxtydcnF10jn1d+UXFRci+kQ3/Hv6wNDNcYNHQ5OXl6W37Ugr2ihgZGan84RSJROA4DgKBQOtRbAB4u4ocvZ6OKTsuq9x+D/KeYsqOy1j9pi/6tXLWy5eu/PJwHAdTU1O4uLgAANzc3NChQwd06dIFvXr1wpYtW/DOO+8AAHJycjBjxgzs27cPxcXF6NChA7777jv4+PgAAMLDw7F3715MnToVixYtQnJyMmQyGbp37462bdti+fLlmD17NiIjI3Hu3Dmldvj4+GD48OGYN28e4uLiMHv2bFy+fBklJSVo164dvvvuO/j6+gIAPDw8AADDhw8HALi7uyMpKYmv/8qVKzh27BgGDx6MjIwMWFtb8/V88MEHuHbtGk6ePAkAiI2NxaxZs3DhwgXY29tj2LBhWLRokd7XT4GtrS1cXFzg4uKCn376CQ0bNkRkZCSaNm2KJ0+e4L333sPvv/8OS0tLzJgxQ8X/1q1b8f333+POnTuwsLBAz549sXz5cjg6OgKQz1KYMmUKjh07hidPnsDV1RWzZ8/GuHHjeB9JSUmYPn06zp07h2bNmuGnn35C586d9e6LvuNGHzuFkBeJRFp/4Kq7n55X6qIvNVGnIXxWxUdly+hjbwgbGqP1s87q+n0RxqioVP6zjBNytSpyaxKBUFAnfamJeqvrU8AxSB5c1WsNtKjgMUrzGQRcA+U6dayRtks5jUZ//ah1DbRzThx89vyiJJQVNlmNuumV30yNCDZ+8hiekQs0rpFW5P/TJxyA5qndnpEL8E/veXD7azUAzSLY6br2qd2Nzv7AZ2qyaZOyBSZaRC4HAEzz9H8OgEnhY435Chvjp5rF+suCujXsivRSCwc8cfFR+g2q637TlM8J9fu9WtdUpm0vxl+DeohUxrDgwE21z8oUaQsO3IRUVnsrEhRrrCMiIvi0119/HQ8fPsSRI0dw8eJF+Pr6olevXsjK+m/a0L1797Bnzx5ERETgypUrKn5DQkJw/vx5JCQk8Gk3btzA1atXMWbMGABAfn4+QkNDER0djePHj6Np06YYMGAA8vPlX5JxcXEAgI0bNyI9PZ1/X55evXrB2toae/bs4dOkUil27tyJkJAQAEBCQgL69euH4cOH4+rVq9i5cydiY2Mxbdq06lw6mJnJIx0lJSUAgE8++QTR0dHYvn07jh49iqioKJV19qWlpfjiiy8QHx+PvXv3IikpCWFhYXz+3LlzcevWLezatQs3btzA6tWrYW+vfKTGnDlzMGPGDFy5cgWvvPIKRo8eTdPRCYIgCMKQyKQQp12Bzb1IiNOuADI1x5c+Oz9ao40OH845cWjzWwiaH/wYTU4uQvODH6P1L2NgnRjN21gnRqP1L2PQ/ODH8IxajG73lqDNbyG8Tfl8dT6cc+LgGbkARuXEOvDfGmjrxGjYJMWgY+JKGD95rNbG9fxarfk2iX+i9b/bAWhew6xtjTQAuJ1eBbczq7TaNIr9HsZPHmld98wxmfZ10YWPYfzksVab5zkirYlSUyuNsXoGgHECrfkl5vYosbDX7kNHXsV8VuHfiukpXafQhnNaoAh7DXE+MQvpuU815jMA6blPcT4xC1087WqtXS1atMDVq1cByCPR58+fx8OHD2FiYgIAWLZsGfbu3Yvdu3djwoQJAOQidcuWLXBwcFDr09vbGz4+PtixYwfmzp0LANi+fTs6d+6Mpk2bApA/LADkkdy8vDysWbMGtra2+PPPPzFw4EDet7W1NZycnNTWIxQK8cYbb2DHjh0YP348ACAyMhI5OTl8ZH7JkiUICQnBhx9+CABo1qwZVqxYgcDAQCxdurRKU+wLCwvx+eefQygUIjAwEAUFBVi/fj22bNmCwMBASCQSbN68Ga6urkrl3n77bf7/TZo0wYoVK9CxY0cUFBRALBbj/v37aNu2Ldq1aweJRMJPtS/PjBkz+DX9CxYsgLe3N+7du8fPniAIgiCIlxodx1vps2u3rl25bZJi0OrGSo3nR+vyYZMUA89E1Yh0+SOtAPW7chs/eax1Z2+FD9ZzLhpqEdIMcqGsbcMwvaLWZ1bonNqtKyJtrEdE2ugFjEiXmlpB9DRXY5QZnADQ8BCCASg1twc4wEjDQwgG+bj799VJaBK5UCWirfjsH7QegQZXf9OYn+I3FYB8PGqyUfxfrf82I2GbcErpfih9dg679e0TSnsJlNI57HpBgr2GeJivWaxXxc5QMMb4qc3x8fEoKCiAnZ3yA4OioiKlaLm7u7tGsa4gJCQEGzZswNy5c8EYwy+//IKPP/6Yz3/w4AE+//xzREVF4cGDB5DJZCgsLMT9+/cr1f6QkBC8+uqrSEtLg4uLC7Zv347g4GB+inx8fDyuXr2K7du3K/VZJpMhOTmZn46uD6NHj4ZQKERRUREcHBywfv16tGnTBvHx8SgpKeGnpgPy6fPNmzdXKn/x4kWEh4cjPj4e2dnZ/LTz+/fvw8vLC5MmTcLw4cNx4cIF9OvXD8OGDUPXrl2VfLRp04b/v7OzfPnEw4cPSbATBEEQ9ZsaOGtaH7Fd8XgrXWdRazu6StvUbV1HZPE+es+D618/AtAupHVN3da1s7c+QlofoaxTbJOQVsnXR0iXWjggpRaEdMqrk5HrGYh/BAKVsV9eHD9p4KWSX2Rki7SAach9Jp7/6ROu0QcArf5TO72rcu/KGIfzwgFo7vIYxk+zNd/3hAok2GsIR0v1G9VV1c5Q3Lp1C40bNwYg34Hd2dkZUVFRKnbl14hbWFjo9Dt69Gh8+umnuHTpEoqKipCSkoJRo0bx+aGhocjMzMR3330HOzs72NnZwc/Pj59iri8dO3aEp6cnfv31V0yaNAm///47Nm3axOcXFBRg4sSJeP/995XKyWQypT7pw3fffYfevXvDyspK5wOLijx58gRBQUEICgrC9u3b4eDggPv37yMoKIjvc//+/ZGYmIiIiAjExsaiV69emDJlCpYtW8b7Kb++RfGgpT4c60cQBEG8oNTjs6b1EduK460yzj/QekTWP73nwe2s9jXSBhHSsd9rjRaTkNZMbQjpIiNbpHWZAs9TX9SskH42bv8RCP67NxT91CGkK0ah1QnpEgsHxDu+Ac7DHwIAOY0DkOPup/E+rZhfYmqDO2n2cPFw5ddL6/Th7geLtHgU3EiA2NsTT1x8/vseEAhR4KJ8LDGkMoATIN+57QuzR0dtQYK9hujU2BbOVqbIyH2qdp0HB8DJyhSdGtfecWUnT57EtWvX8NFHHwEAfH19kZGRAZFIxG/6VlVcXV0RGBiI7du3o6ioCH369FGKZp8+fRo//vgjBgwYgLy8POTm5uLx4wrro4yMIJWqWTdWgZCQEGzfvh2urq4QCARKx8D5+vri5s2b/FR8BYqp+JXByclJxQ8AeHp6wsjICOfOnUNQUBAA+QZyd+/eRWBgIADg9u3byMzMxNKlS+Hm5gYAuHDhgoovBwcHjB49GhMnTkRAQAA++eQTJcFOEARBEAAMIqQNMT28ulFrnRFpLdPDKyO2dU7triUh/SJO7S4WWsJYmv9CRKSvu4aAaxKIf0TCSkek9RHSFW1yGgcgy7ULimOi4OzKqcw+0SWSNdnkOXgj/coDKM2/VCeaNeTLpDIgPU2rjbq8fOe2SEt3hIuzi0GOjSbUQ4K9hhAKOMwf5IVJ2y7xfxwUKL4w5g/yqrHz2IuLi5GRkcEf63b06FEsWbIEAwcOxNixYwEAvXv3RpcuXTB06FB8/fXXeOWVV5CWloZDhw5h2LBh6NChQ6XqDAkJwfz581FSUoLvvvtOKa9Zs2bYunUrfH19kZ6ejoULF/IbuSnw8PBAZGQk/Pz8YGJionQWesV6wsPDsWjRIowYMYJffw8An376KV599VVMnToV77zzDiwsLHDz5k0cO3aM3ym+uojFYowfPx6ffvopzMzM4OHhgblz5yp9UTVq1AjGxsZYuXIl3nvvPVy/fh1ffPGFkp958+ahXbt2cHd3h5GREQ4ePIiWLVsapI0EQRBEPUImBZLPgMtNhV1+EiALAmCkki/KTIRdfhqY7FWl4oYS0gaZHl5NIa0rIq1PVFsfsU1CWhmFUGaQP4ioqtgusXBAvMModExaVWsR6ZoS0vdfnYz0LE+4oPIRaX2FtNoHawIhMi1bwsTTRX2kWZfQVmcjpdmXLzIk2GuQfq2csfpNX4Tvv4mMvP/WqjtZmWL+IC/+SLea4OjRo3B2doZIJIKNjQ18fHywYsUKhIaG8sKS4zgcPnwYc+bMwbhx4/Do0SM4OTkhICAADRo0qHSdI0aMwNSpUyEUCjF06FClvPXr12PChAno0KEDGjZsiMWLF2PmzJlKNt9++y0+/vhj/Pzzz2jYsCGSkpLU1tO0aVN06tQJ58+fx/Lly5Xy2rRpgz///BNz5syBv78/GGPw9PTEyJEjeZvw8HBs2rRJo399+Oabb5Cfn4/Ro0fD0tIS06dPR27uf3/8HRwcsGnTJsyePRsrVqyAr68vli1bhsGDB/M2xsbGmDNnDpKSkmBmZgZ/f3/8+uuvVW4TQRAEYWB0CW19bG7uB45+CuSlQQSgGwC2ajPQ/yvAa7BSvvmz/OI0e/zrN9VgQlqfddaGmB5uCCH9Im5Ipmtqtz4RZ0MI6RS/qWAyBs/IBRptMlqNgNM1LWL7mchN8JyvcnxcTUWka0pIyxgHZJWLKlciIl0tG4KoJCTYa5h+rZzRq4Ujom78iycyIRpIzNCpsW2NRdYBYNOmTUrrurVhaWmJFStWYMWKFWrzw8PDER4erpKuad3706fqN9Fr164d4uLi+KnpEolESUQDwKBBgzBo0CC96q945nt5OnbsiGPHjimllZ8Sn5iYiO7du2ssD8g3qtOGWCzGli1b+L4IBAJ88sknSjajR4/G6NGjNfr9/PPPMXv2bCUfCjw8PFTaYG1tzW+gV9np/QRBEC8dz4Q0Ch4A4gaAe1flSBeTgUuOBYoy1efrEtr62NzcD/w2FiqHGeWny9O7TgPOrFTJNy58/FyusyYhrcaHhQPud3pP6xppfSLOOqPWegppmVSGuMbT4PPwF7XrqLMadUNKQQON+dmNugFZacj28Edu4261FpGuESFNUWniOYEEey0gFHDo6G6lIsqI2ocxhqioKMTGxtZ1UwiCIAh16BLaFWw4MzvVqG05Ic0jcQH6yYU0d/sg+t74GKJyx3SVz9cptEdukb/XZjNiE3Bslmo+AE6RdnaVhnxaZ62Lam9IpscaaYMI6WciNy4pR6MI1rWZ2L96RK31FdIAkG7dEVz3gZA8uqFqI5XpzOehiDRB1Aok2ImXCo7jkJycXNfNIAiCeDlRJ8bLo0Noq7MRAehrZAvOE0DrYZrFdt5/UW3hmZUQasrXKbQ54MinCiWs2ebwdECHmCYhrZpvqA3JdE7t1nONNAAk9JqPhtErNZ4frXP6ty4R/IzyEWdRwWOk/8tg4t8dgmcnxmiNSFdGSOtjQ0KaIOoNJNgJgiAIgqg+uiLjGsQ412cxAAG42weBPeOgUWhriWqblmbJy3KcRrGNClFtVTGor9BmQL6a3ZQr2ugS6/UMQ0wPr7WzprWIbf54q05DUOikex21PtOysz38cSOzscbzow02tbucjUwqQ2ZuGlyq4oMgiBcKEuwEQRAEQWhHJgWXHIuGWWfBJUuAJgF6iXGdU8zz0iHcMw7OHpMhPPa7aj4A6BXVfpaqZ1Rb8y4y9U9o19Y6a0NMD6/ps6b1Edvlj7cyqJDWdX40CWmCIGoIEuwEQRAE8bKjLTr+TIyL8tLQAQCSV+stxnVNMVeI8TYpW8BJ87U0UHdUm6uHYhvm9kBhJtT3Hc+ENFObX9vrrPWdHl6XZ03rJbYrbiRGQpogiOccEuwASktLUVpayv8fAMrKyvgduWUy9WvMFLt4K+w0oY+dLht966rv1FU/aqJeQ/isio/KlKmJMVpWVsbfJ+WpeA89z9RFX2qiTkP4rIqPypbRx94QNi/tGJVJwaWc5cU4c+uiJHq42wchPDYbXDlBzCxdIO27GAAgfDZNXUnoPRPj0tfWQ3j8c5X8Z1by1MPTwWkR0hwYTLWK9dqHmdsBhVn/bQ5XPg8cYOkM+UOEDM02EhdIe38BYcR4AJySneJqyTpPhuCvH9Tky7n/6mRke/iD9ZoPt79+UIpYl1g4IOXVyfLNxgDtNh7+KHBoqd2HVIasRt2Q5doFlg+uwagwC6XmtshvoLxGWh8bAJAxDpmWLWHk4SSPSjNUENMc8hq0KX/R1OzarY+NMrJn+TID7gBeXZ9VKV/ZMvra67Krbv7zRF315UUZo5UtV1tjlEm1/16tL1SmbRzTdX7VC8gPP/yAH374AVKpFHfv3sWOHTtgbm6uZCMSieDk5AQ3NzcYGxvXUUsJov5QUlKClJQUZGRkoKysrK6bQxAEADAZ7AruwLQ0B0+NrJEpbi6P2j7DOScOrf/drrRZVpGRLa65hiDduiOcc+LQMXElAPWR1xKhGMbSAo1TqYuFlvVObBcLLWEszdfYZvZMHGvKLzKyxfWGY9AxaRUA9dclrvE0ANB67eIaT+OvccXPoNDIFtfLfQba8v9zrP2z1stGHx8EQRBEjVNYWIgxY8YgNzcXEolEq+1LKdgV5OXlwcrKCo8fP+YvVGlpKY4fP46AgACkp6fDw8MDpqamasszxpCfnw9LS0twnJYVcXrY6bLRt676Tl31oybqNYTPqvioTBlDjtGioiLcvn0bTZo0gVgsVslX3Dt9+vSB0bMdbZ9X6qIvNVGnIXxWxUdly+hjbwibF22MXt+1GB0e74YgP51PV0TGWYuB8si5uuj4s3d8dDw/TaN4rc2/NszMDigyZFQb6qPar055FtXWcF2Gb/zv+lWYeSCzdIHs2fUF1M9OqGgjT5TPcpDmpCHudgp8h06BkYmpSv7TrGRcuZsBWafOMDNT/Z59npBJZciIz4CTj5Pmdd/PSb3V9VmV8pUto6+9Lrvq5j9P0BitfvnKlKutMVpUXITsG9nw7+EPSzNLvftS2+Tl5cHe3l4vwU5T4gEYGRmp/HgTiUTgOA4CgUDj2emKqcMKO03oY6fLRt+66jt11Y+aqNcQPqviozJlDDlGFUJeJBJpFTvq7qfnlbroS03UaQifVfFR2TL62BvC5rkZo1rWlXO3D/LR3fJw+ekQ7RknXzd+fDa0HTsmOjpT65pvg4p1LWu5GTgUGdnAuP83ED2bPq5sx8nb0v8r+dvfxqrYKP7H9VsKkddgQGSksgkeJ3EB+i2F0Gswyhp2QOn+j5Wi2op8keL4uNbDAO/BQPIZlOWm4q/rSej8+ofKQlsfGwCAEdC0B1hpKTJTD8PIxLTCGJTnywrzkJl+CrZGoudeDCkQCAV10peaqLe6PqtSvrJl9LXXZVfd/OcJGqPVL1+ZcjU9Rjmhfr9X65rKtO3FuNOIKsNxHPbu3cu/v337Nl599VWYmpqibdu2GtPqG5s2bYK1tXWlynh4eGD58uU11KLao3v37vjwww+V0u7cuQMnJyfk5+s3VbWkpARt2rTBhQsXaqKJBEFUlpv7geWtgM0DgT3j5f8ubyVPl0khPDYbgDpR/Uy6Hp6uvGO7OjtDbdBmbq+2JXI4QNIQCP72v/cV8wFcdw0BazlYfnSbxFnZROIiT/caLH+psSkysoV0+Mb/zmr3Ggx8eB0IPQgMXy//98NrfD5rMRDHvP+Hsjf3qs3nEQiBxv5g3sORadlSdcMzfW0IgiAIooqQYK8NZFKIUs4C13cDiTHyqEkNEhYWBo7jwHEcjIyM0KBBA/Tp0wcbNmxQ2VAsPT0d/fv359/Pnz8fFhYWuHPnDiIjIzWm1SXqhPaoUaNw9+5dg9YTHh7OX0eRSAQPDw989NFHKCgoqLZvDw8PcBwHoVAIZ2dnNGnSBCNHjsTJkycN0HJg1qxZmDZtGiwt5VOBoqKi+L5wHIcGDRpg+PDh+OeffwAAxsbGmDp1KmbNmmWQ+gmC0IFMKv97cE3N3wXFrusVBbdi1/XoZeA0TGOXU1u7pesnxtFvKeA9VKMYlw7f+N9abR1CW51N2Zt7cdz7f8pT0AFeSKP1CPm/FYU0JwBz76Y5nyAIgiDqATQlvqa5uR/c0U8h1nQ2bQ3Rr18/bNy4EVKpFA8ePMDRo0fxwQcfYPfu3di/fz9EIvlH7+TkpFQuISEBwcHBcHd315pWWUpKSmp08z4zMzOYmZkZ3K+3tzdOnDiBsrIynD59Gm+//TYKCwuxZs0aFdvK9nHhwoUYP348MjMzkZmZiR07dqB379744osvMGfOnCq3+f79+zh48CBWrlSdLnvn4qRY3AAAIABJREFUzh1YWlri77//xoQJEzBo0CBcvXoVHMdh5MiRmDt3Lm7cuAFvb+8q108QhA60nVneIliep+088nOrDdcWrceOcYCZDVCUXa7+cnmAXIx7DQa4LRr6tFQ56t0iWGWaP5PKgH8O/1dOIbS1Uc6GlZYCNw5rtycIgiCI5xSKsNckuqIkN/fXWNUmJiZwcnJCw4YN4evri9mzZ2Pfvn04cuQINm3axNuVnxLPcRwuXryIhQsXguM4PsJcMQ0AUlJSMHLkSFhbW8PW1hZDhgxBUlIS7zcsLAxDhw7FokWL4OLigubNm/Plxo0bB1tbW63lli1bBmdnZ9jZ2WHKlCn80Qfdu3dHcnIyPvroIz5aDKhOiU9ISMCQIUPQoEEDiMVidO7cGVFRUZW+jorTAlxdXTFq1CiEhIRg/37557ZgwQL4+/tj3bp1aNy4scbNCTVhaWnJn0QQEBCAtWvXYu7cuZg3bx7u3LnD212/fh39+/eHWCxGgwYNMHbsWGRmZmr0+9tvv8HHxwcNGzZUyXN0dISzszMCAgIwb9483Lx5E/fu3QMAWFtbw8/PD7/++mul+kEQRCXQI3qucyo7L6B1YICp6hj0ve5p6oB+kXFAd9SbIAiCIAglSLDXFDIpHyXRuMbw6Gc1Pj2+PD179oSPjw8iIiLU5qenp8Pb2xvTp09Heno6ZsyYoTattLQUQUFBsLS0RExMDE6fPg2xWIx+/fqhpKSE9xcZGYk7d+7g+PHjOHjwIEpLS3nh+eeff2osd+rUKSQkJODUqVPYvHkzNm3axD9kiIiIgKurKxYuXIj09HSkp6dX7AYAoKCgAAMGDEBkZCQuX76MoKAgjB49Gvfv36/WNTQzM1Nqa2JiIiIiIhAREYErV65UyzcAfPDBB2CMYd++fQCAnJwc9OzZE+3atcOFCxdw9OhRPHjwAOPGjdPoIyYmBh06dNCrLwCU+tOxY0fExMRUsxcE8ZKjabp7ub8LqjxL0zN6zkxt1HqRY5ip6krrxj+8jrI39+KC+yT5um8S4wRBEARRK9CU+Joi+YzuKEleqtxO19Q/A9KiRQtcvXpVbZ6TkxNEIhHEYjE/VV4sFqukbdu2DTKZDOvWreMj3Bs3boS1tTWioqLQt29fAICFhQXWrVvHTxNXlFuxYgWsrKwgEAjUlrOxscGqVasgFArRokULBAcHIzIyEu+++y5sbW0hFAr56LQmfHx84OPjw79fuHAh9uzZgwMHDmDatGlVunYXL17Ejh070LNnTz6tpKQEmzdvRoMGDarksyK2trZwdHTkZx2sWrUK7dq1w+LFi3mb9evXw93dHXfv3kWLFi1UfCQnJ+sU7Onp6Vi2bBkaNmzIz34AABcXFyQnJxukLwTxUqJturuZjcGi57JOEyCI/oo/U/w/DDdVXUlwC4Rg7t2QeiMPPu7dSIwTBEEQRC1Bgr2mKHhgWDsDwRir9jnk8fHxuHfvHr+hmYKnT58iISGBf9+6dWulNd2Kcm5ublrLeXt7Qyj878egs7Mzrl27Vqk2FhQUIDw8HIcOHUJ6ejrKyspQVFRU6Qj7tWvXIBaLIZVKUVJSguDgYKxatYrPd3Nzg4ODQ6V86qL8ZxQfH49Tp06pPfc8ISFBrWAvKirSOD3f1dUVjDEUFhbCx8cHe/bsgbGxMb8ZoampKQoLCw3YG4J4iVBMd68Y+1ZMd391kn5+zGyAohxVPwDw7Kxxmd/HuJhSiI6P9wD51RTjgH7rxgmCIAiCqHVIsNcUYj0jrvraGYhbt26hcePG1fJRUFCA9u3bY/v27Sp55cWrhYWF2nKrV6+GWCxWOu+7fLmK5xJyHKeyu70uZsyYgePHj2PZsmVo2rQpTExMMHz4cKXp3/rQvHlzfpM+FxcXlU3lzM3NK+VPF5mZmXj06BH/GRUUFGDQoEH46quveBuZTIaCggI0a9ZMrQ97e3tkZ6uP0sXExEAikcDR0VHlgQsAZGdnG/wBBEG8MGg5F133dHcOuPqbfvV0ngRELYG688gByAW5QIh0644oe+NzGKXFkRgnCIIgiBcUEuw1hXtXeaQjLx3aoiRw71prTTp58iSuXbuGjz76qFp+fH19sXPnTjg6OkIikVS6nL29PVxdXZUEe2UwNjaGVKp97f/p06cRFhaGYcOGAQDy8vKqtH7d2NgYTZs2rVI7q8L3338PgUCAoUOHApBfsz179sDDw4Pf2V8mkyEvL0/lgYiCdu3a4ebNm2rzGjdurPW8+uvXr6Ndu3bV7AVBvIBom+ruNVi/ZVCFj3Xvyi5xAQJmAI4ttU9lf7YRJ4lxgiAIgnixoU3nagqBUP5DDuq2nVOOktQExcXFyMjIQGpqKi5duoTFixdjyJAhGDhwIMaOHVst3yEhIbC3t8eQIUMQExODxMREREVF4f3338e///6rs1xISEilylXEw8MD0dHRSE1NxePH6s8abtasGb8RXHx8PEJCQsCY5i2a6oL8/HxkZGTg33//RXR0NCZMmIAvv/wSixYt4h8STJkyBVlZWRg9ejTi4uKQkJCAP/74A1OmTNH40CIoKAhnz57V+VBDHbGxsfxeAgRBPEOfEz/0Xd7UZuSz/+j4u6DvrusEQRAEQbzQkGCvSbwG63ccTg1w9OhRODs7w8PDA/369cOpU6ewYsUK7Nu3T2l9eFUwNzdHdHQ0Gv2fvXuPj6K6+wf+mb3kRrIBEiEJUKCo0A3hoohCxeJPA+FaBW9EvLZoeSheqE+lWAv0kT7Qx/qiL41YKxcpRaViFVCBaLWioBGQKEQMxRARkmASkk3MbXdmfn9MdslmbzN7383n/Xrxgsx+z5kz4eRyds58vz/4AebMmYMf/ehH+NnPfoa2tjavd9xTUlLw/vvvY+DAgbjppptUt+vu97//PU6dOoVhw4Z53L791FNPoU+fPpg4cSJmzZqFqVOnYtSoUZqv1R+bNm1SlSfgd7/7HQYMGIDLL78cd911FxobG/Huu+/i0UcfdcTk5OTgo48+giiKmDJlCvLy8rBkyRJH0j53pk2bBoPBgHfeeUfTuEtKStDY2IibbrpJUzuiuBBIZvfdS4FeKh8lGT5d/c8FZl0nIiLq8bglPtTMsyFfOg3ff/kOUuQm6NKy3T9jGERdy6D50v2us7vSZO6OZWVl4cUXX/Q6BneysrKwbt06mEwmtwtOd+3Wrl3r9PFVV12F0tJSp2N333037r77bsfHQ4YMwb/+9S/Hx5IkYf78+U5vDHSt/+7OihUrHHXn3Vm+fLnbxwsqKirwk5/8xGvf9nPbt7d7+nwAF3YL2Nnb2N8U6F5f3mAwYNmyZXjqqaeQn58PQKlf72uHwXPPPYdHHnnEUe6NqMcINLO75Qwgy+ofg9Lp1SWCIyIioh6PC/Zw0OlhGzQBMJkAP5/bptjx9ttvO2WSj4T7778fDQ0NaGpqUhXf0dEBs9mMhx56KMQjI4oywcrs3lKrLPC33QlfyeIA8NlzIiIiUoWrR6IgKykpwfjx4yM6BoPBgMcee8xtJnh3EhISeHedeh41293VZnZP7R/Rx6CIiIgoPvEOOxERxbfOcmxC4xlkNJ0CpKkAjMHN7G6v+KG27jkRERGRClywExFR/OryfLoBwNUA5GdeBKatAcQOdX2MugX4eB1UbXUHuN2diIiIgoZb4omIKD55KsfW1Pl8et1Jdf1oyexOREREFES8ww7AarXCarU6/g0ANpsNsixDkiRIkuS2nT3rtj3OEzVxvmLUnivaReo6QnHeYPTpTx9a2oRijtpsNsfXSVfdv4ZiWSSuJRTnDEaf/vShtY2aeM0xkgjD28rz6a4Vz2Xl6KFNQFo20FQNwc12d7lzu7st5wrlrvmwKRBOH3BsdZcHTVCOR2DOx8scDUa/MTtH3bDZbAAAWZQhibH7sx6AY/zhvo5QnDfQPv1pr7WN2nhfcYG+Hks4RwNvr6VduOaoLHr/fTVaaBmbIPuq9RSHioqKUFRUBFEUUV5ejq1btyIlJcUpxmAwICsrC4MGDUJCQkKERkoUPTo6OnD69GlUV1c7fqkkiihZQkbzV0iyNqDN2Bt1qcMBQdk4ltH0Ja7+z//67OLLrDkYUa2UTey6sLf/YPx06GJU9b4iyAMnIiKinqylpQWFhYVobGx0KjvtTo9csNtZLBakp6ejtrbW8YmyWq0oLi7GNddcg6qqKgwZMgRJSUlu28uyjKamJqSlpTlqYvsb5ytG7bmiXaSuIxTnDUaf/vShpU0w52hrayuOHz+OH/7wh0hNTXV53f61k5+fD6PRqOpaolUkriUU5wxGn/70obWNmvjuMcLxXdDvXQah6cJ2dyktGwczb8LIm5choXwHDK/f7/Pcthv+AugTXfqSTQMg5q+CPGKmiisOv3iZo8HoN1rnqD8xTa1N2PfePvTJ7YPkxNiu2iGJEqpLq5E1Ogs6ffiewAzFeQPt05/2WtuojfcVF+jrsYRzNPD2WtqFa462trfi/LHzmHTtJKQlq6uWFAkWiwWZmZmqFuzcEg/AaDS6/OA0GAwQBAE6nQ46D7XT7VuH7XGeqInzFaP2XNEuUtcRivMGo09/+tDSJphz1L6QNxgMXn/Bdff1FKsicS2hOGcw+vSnD61t1MQbjUYYT7wNbL8H3bO2C03VuKLpaYgnL4chfYCqcxrSBygJ4nJnO2V2FwZPhCEGMrvHyxwNRr9RNUf9jDFYlV/LBL0Q84shO51eF5FrCcV5A+3Tn/Za26iN9xUX6OuxhHM08PZa2oV6jgp6db+vRpqWsXHB7ocOsQM2yQZJktBibYHBavC5GPIV5y3GoDPAIPC/iojIW+105dl0QF/8GPDAESUpnKXKbaxLOTZmdiciIqIoxFWgRh1iB0q+LUGztVlZZLe0IOV8iu8Fu484bzGpxlSMyxmneox33303Ghoa8PrrrzuOHThwAFdffTUKCgrw5ptvOsWfOnUKQ4cOdXzcp08f5OXl4YknnsCkSRd+gV2xYgVWrlzpcr7i4mJcf/31qsdHROQv4fQBr7XTBQCwnAFOfwIUrFGywXcrxyZDUOK6l2MjIiIiijJcsGtkk2xotjYjQZcAo9EInVWHXom9oBO8LNhlyWecp5h2Wzuarc2wSYEl+Vq/fj0WL16M9evX4+zZs8jJyXGJeeedd5Cbm4va2lqsWrUKM2fORHl5Ofr37++Iyc3NxTvvvOPUrm/fvgGNjYjIhSQ6bVFHTmfit+Yade2ba4C8m5Sya5112B1MOcpineXYiIiIKMpxwe6nREMiEvQJEA0ikgxJPhfsvuK8xXR0dAQ01ubmZrzyyis4ePAgqqursWnTJixbtswlLiMjA1lZWcjKysKyZcvw8ssv45NPPsHs2Rd+qbVnzyciCpmyHS6LbENaDrIz5wK516nrI7XzjUbzbGDEDKByP2yNZ/Dx0VO48uaHYEx0n0yUiIiIKJrER7YI8mrbtm0YMWIEhg8fjvnz52PDhg3wVhygtbUVmzdvBgCWtCOi8CrboWxj777tvakKV1Q8DbTUK3fIXaqrK2QoGd4dz6YDjufT5dy5qEv7EbfBExERUczggr0HWL9+PebPnw8AKCgoQGNjI/7973+7xE2cOBGpqano1asXnnzySVx++eW47jrnu1lffPEFUlNTHX/Gjx8flmsgoh7AR0I5ANC/8zgw9X8dR7uSOz8W81dxUU5ERERxgQv2OPfVV1+hpKQE8+bNA6Bsab/11luxfv16l9hXXnkFn332GbZv346LL74YmzZtcik5MHz4cBw5csTxZ/v27WG5DiKKf2oSygmWM0BKhvJsuinbOcCUg0+HLo7a2ulEREREWvEZ9ji3fv162Gw2pyRzsiwjMTERzzzzDNLT0x3HBw0ahEsuuQSXXHIJbDYbbrzxRhw9ehSJiYmOmISEBFx88cVhvQYiiiPdk8l13bquNaFc57Pp9r5sOVegavcejA3NyImIiIjCjnfY45jNZsPmzZvxpz/9yemueGlpKXJycvDSSy95bHvTTTfBYDDg2WefDeOIiSiule0A1o4EXpwJbP+Z8vfakRCO71JeT+3vvb2dPc5eOz3vJuVvboMnIiKiOMMFexzbtWsXzp8/j5/97GcYOXKk05+5c+e63RZvJwgCHnjgAaxevRotLS1hHDURxSVPyeQsVdBvvwfZDZ9CHjRBe0I5IiIiojjGBbuf2m3taLO1hfxPu63d7zGuX78e119/vdO2d7u5c+fi4MGD+Pzzzz22v+uuu2C1WvHMM8/4PQYiIm/J5OzHRn77d+XDgjWdx5lQjoiIiIjPsGtk0BmQakxFs7UZbbY2tFhbILVL0Om81GGXJJ9x3mJSjakw6AywwaZqjJs2bfIZM378eKfSbu7KvKWkpKC+vt7x8YoVK7BixQpVYyAicqjc7yOZnIwUaz1spw8oddNv2exShx2mHHyaMQdjmVCOiIiIehAu2DVK0Cdg/MDxsEk2SJIEi8UCk8nkc8HuK85bjEFngEEwoA1tQb0WIqKg6kwoJzSeQUbTKUCaCsCoLZkcoCzamVCOiIiIiAt2fyToE5CgT4AkSbAZbUgxpvhcsPuK8xUjSVLQxk9EFHRlOxx3xQ0ArgYgP/MiMG2N9mRywIWEcnZWazBHS0RERBQT+Aw7EREFxlNCuaYq5fj3dT6SyQloMfZVks4RERERkQMX7ERE5D8vCeUE+7G9y4Cp/+s42j0KAI4OvJ3J5IiIiIi64YLdA0FQfol0l4yNqCeyfy3YvzaIAPhMKAfIgOUMkJKhJJMzZTu/bMqBOHcjqnpfEdJhEhEREcUiPsPugcGgfGpaWlqQnJwc4dEQRZ7VaoUsy9DreReUutCSUC7vJpdkchg8EbIoAV+/FdpxEhEREcUgLtg90Ov16N27N86dOwdAKXHW/c6iJEno6OhAW1ubz6RzvuJ8xag9V7SL1HWE4rzB6NOfPrS0CdYclSQJ3333HVpaWrhg74lkCULlh0BrnWOR7di+rjWhXPdkcgAgMqkmERERkTtcsHuRlZUFAI5Fe3eyLKO1tRXJycletwmrifMVo/Zc0S5S1xGK8wajT3/60NImmHNUEAQ0NjbG9Pwj7YTjuzDl2BIYjtRfOGjKAQrWKOXXBk9UPrZUwd1z7ICgvD54YriGTERERBQ3uGD3QhAEZGdno1+/frC6KSlktVrxwQcf4JprroHRaPTYj5o4XzFqzxXtInUdoThvMPr0pw8tbYI5RwVBwFdffaVqjBQnynZAv/0e6LsvxC2d2d9v2aws2gvWKB9DQNdFuwxBSSlXsJoJ5YiIiIj8wAW7Cnq93u02YL1eD5vNhqSkJK+LITVxvmLUnivaReo6QnHeYPTpTx9a2gRzjrp704riWJfs7272/AAQgN1LlWfSzbOVxXtnHXYHU46yWDfPDtuwiYiIiOIJF+xEROSqM/u7lwcplOzvlfuVZ9LNsx0J5WyNZ/Dx0VO48uaHYExMCuOgiYiIiOILF+xERORKS/Z3u86EcrLVirrKt7gNnoiIiChAsZtunIiIQkdr9nciIiIiCjreYSci6qkk0aUmuuOueGf2d9lSBYHZ34mIiIgiggt2IqKeqGyHhyRxneXadHpH9vfOFHNddH7E7O9EREREIcUFO5Ts1/YM2N3/9tVOTayaOF8xWsYVzSJ1HaE4bzD69KcPztHQi8S1hHOOCsd3Qb/9HnTPAC93lmsT526EPGImcMk0SDf8FdKbv0ay9UIddtmUAzF/FeRLpgEeSl5quZZgzD81MZyj0XnOQPsN9fdRtfHBiLHZbAAAWZQhiZKqsUUr+/jDfR2hOG+gffrTXmsbtfG+4gJ9PZZwjgbeXku7cM1RWVR2Bdpstqj+ea9lbIIsy+72Osa1oqIiFBUVQRRFlJeXY+vWrUhJSYn0sIiIQk+WMOXYEiRZ691mgJcBtBr7ojj3KUDQOdpkNH+FJGsD2oy9UZc6/MJrRERERKRJS0sLCgsL0djYCJPJ5DW2Ry7Y7SwWC9LT01FbW+v4RFmtVhQXFyM/P99njWu1sWrifMVoGVc0i9R1hOK8wejTnz44R0MvEtcSrjkqVH4Iw5YbfLa1zX8d8uCrQz5H1cZzjjqLlzkajH7jaY42tTZh33v70Ce3D5ITk1VdS7SSRAnVpdXIGp0FnT58b/CF4ryB9ulPe61t1Mb7igv09VjCORp4ey3twjVHW9tbcf7YeUy6dhLSktNUX0u4WSwWZGZmqlqwc0s8AKPR6PKD090xLe39jfMVo2Vc0SxS1xGK8wajT3/64BwNvUhcS8jnaGudqjaG1jqgyzhCPUfVxnOOOouXORqMfuNhjhqsyq9lgl6I+cWQnU6vi8i1hOK8gfbpT3utbdTG+4oL9PVYwjkaeHst7UI9RwW9sn/QYDBE9c96LWOLj680IiJSh+XaiIiIiGIG77ATEcWTLqXahOQMQO6WlKWzXBssVQDLtRERERFFNS7YiYjiRbdSbQYAU4x9IQwDkHejEtOlXJtSnq3rop3l2oiIiIiiCbfEExHFg7IdyiK8a111AEnWeqWEW9mOCwfNs4FbNgOmbOc+TDnKcfPsMAyYiIiIiHzhHXYiolgnicqddTdb3B330HcvBUbMuHDn3Dxb+bhz+zxS+yvb4HlnnYiIiChqcMFORBTrKve73FnvSoAMWM4ocUMnXXhBp3f+mIiIiIiiiqYFe0NDA/75z39i3759qKysREtLCy666CKMHTsWU6dOxcSJTFJERBR2zTXBjSMiIiKiqKDqGfazZ8/i5z//ObKzs/HEE0+gtbUVY8aMwXXXXYeBAwfivffeQ35+PsxmM1555ZVQj5mIiLpiqTYiIiKiuKTqDvvYsWNx11134dChQzCbzW5jWltb8frrr2Pt2rU4ffo0HnnkkaAOlIioR+tSrs3leXMfpdpkCBBYqo2IiIgo5qhasJeVlSEjI8NrTHJyMubNm4d58+ahrq4uKIMjIiK4lGsDoCzQC9YoyeO8lGpz/Iul2oiIiIhijqot8b4W64HGExGRBx7KtcFSpRy3l2vzUKqt1dgX4tyNLNVGREREFIM012F/8cUX8eabbzo+/vWvf43evXtj4sSJqKysDOrgiIh6NC/l2hzHdi9V4gBlUf7QUeCuXcDc9bDNfx3FuU9BHjEzXCMmIiIioiDSvGD/wx/+gOTkZADAgQMHUFRUhD/+8Y/IzMzEww8/HPQBEhH1WD7KtaFruTY7e6m2vJsgD74aEDR/myciIiKiKKG5Dvvp06dx8cUXAwBef/11zJ07F/fddx9+/OMfY/LkycEeHxFRz8VybUREREQ9muZbL6mpqY6kcnv37kV+fj4AICkpCa2trcEdHRFRT8ZybUREREQ9muY77Pn5+fj5z3+OsWPHory8HNOnTwcAHDt2DEOGDAn2+IiIei4f5doAQXmd5dqIiIiI4pLmO+xFRUWYMGECvvvuO2zfvt2REf7QoUOYN29e0AdIRBS3JBGo2Ad88SqEyg8BWXJ+3V6uDYBSrq2rzo9Zro2IiIgobmm+w967d28888wzLsdXrlwZlAEREfUI3WqrGwBMMfaFMAxA3o0X4uzl2tzWYV8dc+XaRElGSUU9zjW1oV9aEsYP7RvpIRERERFFLVUL9s8//xwjR46ETqfD559/7jV21KhRQRkYEVHcstdW77bNPclaD2y/B9DrnRfi5tnAiBlKNvjmGuWZ9cETY+7O+u6jVVi5swxVjW2OY9npSXhs2vAIjoqIiIgoeqlasI8ZMwbV1dXo168fxowZA0EQIMsXftG0fywIAkRRDNlgiYhinpfa6oL96O6lygK964LcXq4tRu0+WoWFWw67XHV1YxsWv1yKey4VML3L8e534scOTAvncImIiIiigqoFe0VFBS666CLHv4mIyE8+aqsLXWurx9gC3b7Irmr4Hl83ChAlGcbO4yt3lrlNmydDeaPitVM6/Loz3t2d+CxTIqZncVFPREREPYuqBfvgwYPd/ru7rnfdiYjIjRisrS7JwCcV9ahrsTmeO9frnJPguS6y9Xj1Tx9gxexcpCcnOC2+u5MBNHQIOFh5Hs0dkts78TWWdmyw6HDZsRrMHDNQ9aKeiIiIKJZpTjp39913o6ioCL169XI6furUKdxxxx3Yt29f0AZHRBR3Yqy2+p5jNVh5WI+Gjw86jmWnJ2H5LDMKRmYD8LzdvcbSjoVbDuPeHw9Rda5qSxv+VPwfj3fiAWDV28eh0+mxaKvvRT0RERFRrNNc1q20tBSjRo3CgQMHHMdefPFFjB49GpmZmUEdHBFR3LHXVncp06aQIQCmAVFRW3330SosfrkUDR3Ox6sb27Bwy2HsPlrlc7s7APzzyBlV56v/vsPrnXhAQFVjO377xlGfi3pRuhAhSjIOnKzDzs+rcKJzqz4RERFRLNB8h72kpATLli3D5MmT8atf/Qr/+c9/8Pbbb+Opp57CggULQjFGIqL4Ya+tvu1OdEkzB3T9VxTUVndeiDu/uWB/7nzlzjKkJRl9bnev/96Kvr0ScP77DrcLbQFAeoKMvr0SVI2t/vsOL68qi/qSinpMGJbhdau+fYcAERERUbTSvGA3Go34v//7P6SkpOB//ud/YDAY8O9//xsTJkwIxfiIiGKLJPouv+ahtnqrsS8SZj8FQ5hqq7uriW5/Nr2kot7nQryqsQ0HTtapOtcNY3Kw8aNT3d6iuPBWwJwhErJMSX5dhzvnmtp8btVfN/8yLtqJiIgoqmlesFutVixduhRFRUX4zW9+gw8//BBz5szB+vXrMX06U/0QUQ9WtsNlEQ5TjnJHvfsivFttdVtyBoqPNmD6iJlhGaqnmuj2Z9PPNXnbmt6Vuu3l+eYsjB/a1zVRXGcddrHyEMYN7oPs9CRUN7Z56FW5C1//vdXn+TJ7JeKRV0u9ZqZfubMM+eYs6HWC1zcviIiIiCJF84J93LhxaGlpwfvUn5iWAAAgAElEQVTvv4+rrroKsizjj3/8I+bMmYN7770Xzz77bCjGSUQU3cp2dG5z77ZEtFQpx2/Z7Lpo71JbXbZagWNvhWWo3mqi2+8890tTd7d7wg8zsf3wGY+LbAHKoty+AM43Z7ksjCXRhrcqAb1OwPJZZizcctjtnXgZwIoZP8L/7in3uqjPTk8CBKjaIVBSUY/G1g6vb14QERERRYrmpHPjxo3DkSNHcNVVVwEABEHAo48+igMHDuCDDz4I+gCJiKKeJCp31r2lQtu9VIkLMXuCtTeOnMEnFfXonl9NTZK4lTvLcHnn3W5P95gFKIvaq4ZlYPkss+NY9xgAWD7L7LhbrdcJmDAsAz8dMwAThmW43MUuGJmNdfMvQ1a68xsGWemJuPdSCdPysnye77FpI1Db3O5h5M6Ky6qxcMthl8V918R6RERERJGi+Q77+vXr3R4fO3Ys9u7dG/CAiIhiTuV+523wLmTAckaJ67yjHgrutrn3TtDDOORCmTO1z6YfqjzvuNt9YRO5ovtC3L7Idt3unojls7QndysYme1yJ37swDTs2f2243VP55vWvwVTc/vj4DcWVed6/chZ1dvmiYiIiMJN84Ldnb179+KFF17Azp070draGowuiYhiR3NNcOP84Gmbe0MHsPjlUhgMek3Ppp9rasNPxwzA07eNxm9fO+JU2i3LzXbxrovsqobv8fWxI/jlrdcgKVFd5vfu7Hfi7axW5+fWfS3qxw/t6/V5eAFAn15Grxnnu26b7zoWIiIionDxe8FeWVmJDRs24MUXX8T58+cxbdo0bN68OZhjIyKKDan9gxunkbdt7vb74fY7xWqfTbfHTc3tD+spEReZr0Jdi81rQjb7IttqNeGtbz8L+V1pb4t6X8/DA8CNYwZg/UenfJ5HfQI+IiIiouDStGDv6OjAa6+9hhdeeAEfffQRrr/+enz77bf47LPPkJeXF6oxEhFFt8ETlWzwliq4f45dUF4fPNHvUwSjBFtJRb2qO8/2JHF2OgG4cmhfGI1Gv8cfCb626qcnJ6hasNvfvGAmeSIiIgo31Qv2xYsX46WXXsIll1yC+fPn45VXXkFGRgaMRiP0er3vDoiI4pVOr5Ru23Yn4Ol+bsFq13rsKgWrBNu5pjZVd567JomLdd626ouSrPrNC1//B0REREShoDpL/Lp163D//fdj7969WLRoETIy+DwfEfUAkghU7AO+eFX521Omd/NspXSbqdvizZTjvqSbSvZn071lMde6zd1zJvYkrJt/WdwtQO1b52eNysYl6bJTxno1Ge6ZSZ6IiIgiRfUd9r/97W/YsGEDsrOzMWPGDNxxxx2YNm1aKMdGRBRRwvFdQPEy5wzwphzlbrq7Bbh5NjBihpINvrlGeWZ98ES/76z7KsFmz2L+7/++VvM2d3dJ23riFm/P2+aVu+f55ixcveZfzCRPREREEaF6wT5v3jzMmzcPFRUV2LRpExYtWoSWlhZIkoSysjKYzeZQjpOIKKyyGz6FfvszcHkm3VKlbH33dNdcpw9a6TZ/SrB13+ZuX1a62+bePWlbT+XtzYsDJ+tU5wfg55KIiIiCTfWWeLuhQ4di5cqVOHXqFLZs2YK5c+di/vz5GDhwIB544IFQjJGIKLwkEXnf/h3uE8h1Htu91PP2+CDR8my6p23uvROAp28bHXfb3IPN/ubFT8cMwIRhGY43N7T8HxAREREFm99l3QRBwNSpUzF16lTU19dj8+bN2LhxYzDHRkQUEcLpA0i21nuJkAHLGWXre5Duprvjz7PpXe8UZ6QY8F3Zx5iaG5pycj2B1v8DgNnkiYiIKHj8XrB31bdvXzz00EN46KGHgtEdEVFkNdcEN86Drgu7jBQDpG439P0pwdZ1m7vVasVbXwY0xB5P6/8Bs8kTERFRMAVlwU5EFFdSVd6RVhvnhruFXe8EPYxDajBzzEAA6HEl2KKRlv8De0b/7gt7ezb5eMzAT0RERKGl+Rl2IqJ4Jw+agFZjX8guxb7sBMA0QMkA7wdPpdoaOoDFL5c6lQnraSXYopGa/wNfGf0BJZu82H0bBREREZEXvMNORNSdTo8vBt6OKyqeATzdVy1Y7Ve5Nm8LO3vf3cuEsQRb5Pn6P1Cb0Z/Z5ImIiEiLmF+wnz59GnfccQfOnTsHg8GAxx9/HDfffHOkh0VE0UoSVdVJr+p9BcS5G2FwW4d9tfuSbir4u7BjCbbI8/Z/wGzyREREFAqaF+yff/652+OCICApKQk/+MEPkJiYGPDA1DIYDFi7di3GjBmD6upqXH755Zg+fTp69eoVtjEQUYwo2wHsftTNAnyN2wW4PGImkDtb1QJfLS7s4pM/2eSJiIiIfNG8YB8zZgwEwfM2TKPRiFtvvRV/+ctfkJQU+l9MsrOzkZ2tPMOZlZWFzMxM1NfXc8FORM7KdgDb7oRLbXVLlXL8ls3u75rr9EEt3caFXXzyJ6M/ERERkS+ak87985//xCWXXILnn38eR44cwZEjR/D8889j+PDh2Lp1K9avX49//etf+O1vf6uqvw8++ACzZs1CTk4OBEHA66+/7hJTVFSEIUOGICkpCVdeeSVKSkrc9nXo0CGIoohBgwZpvSwiimeSqNxZ95YSbPdSJS5AoiTjwMk6vHHkDA6crHNJMmZf2HlJZ4dsLuxijj2bPACX/1tm9CciIiJ/ab7DvmrVKvz5z3/G1KlTHcfy8vIwcOBAPP744ygpKUGvXr3wq1/9Ck8++aTP/r7//nuMHj0a9957L+bMmePy+iuvvIIlS5bgueeew5VXXom1a9di6tSp+Oqrr9CvXz9HXH19Pe6880789a9/1XpJRBTvKvc7b4N3IQOWM0pcAHfT1dTg9lYmTPlI4MIuRtmzyXefA1msw05ERER+0rxg/+KLLzB48GCX44MHD8YXX3wBQNk2X1VV5RLjzrRp0zBt2jSPrz/11FNYsGAB7rnnHgDAc889hzfffBMbNmzA0qVLAQDt7e244YYbsHTpUkyc6LnMUnt7O9rb2x0fWywWAIDVaoXVanX8u+vf3qiNVRPnK0bLuKJZpK4jFOcNRp/+9ME5qp3QeEbVNztb4xnIbr4fqLHnWA0Wv1zqsQb307eNxtRcpW77dcMz8fRto/HEW8dRbbnwPal3ArDypyNx3fDMoHweY2GOqo2PlTl63fBMTL5kEg5Wnse5pnb0S0vEuMF9oNcJTucVJdltjFqR+HoL1TkD7Tee5qjNZgMAyKIMSZRUjS1a2ccf7usIxXkD7dOf9lrbqI33FRfo67GEczTw9lrahWuOyqLym5jNZovK30nttIxNkGVZU1HYsWPHYvTo0Xj++eeRkJDgOOGCBQtQWlqKzz77DB999BHmz5+PiooKTQMXBAH//Oc/ccMNNwAAOjo6kJKSgldffdVxDADuuusuNDQ04I033oAsyygsLMTw4cOxYsUKr/2vWLECK1eudDm+detWpKSkaBorEcWOjKYvcfV//tdn3IcX/wZ1aT/S3L8kAysP69HQAbhuiAYAGb0TgOWXiei6JpNk4KRFgMUKmIzAMJMM3liPf6V1Al47pUNDx4X/7N4JMuYMkTA6g3XaiYiI4l1LSwsKCwvR2NgIk8nkNVbzHfaioiLMnj0bAwcOxKhRowAod91FUcSuXbsAAF9//TX+67/+y4+hO6utrYUoiujfv7/T8f79++P48eMAgI8++givvPIKRo0a5Xj+/W9/+xvy8vJc+vvNb36DJUuWOD62WCwYNGgQpkyZ4vhEWa1WFBcXIz8/H0aj0ev41MaqifMVo2Vc0SxS1xGK8wajT3/64Bz1gzQV8jMvAk1VENw8xy5DAEw5uPLmhwCdXvO1fFJRj4aPD3qJENDQAVxkvgpXeng2vafOUbXx8TJH9xyrwcYDrjsxGjsEbCzXO+3E8CYS1xKqcwbabzzN0abWJux7bx/65PZBcmKyqmuJVpIoobq0Glmjs6DTa06ZFFXnDbRPf9prbaM23ldcoK/HEs7RwNtraReuOdra3orzx85j0rWTkJacpvpaws2+01sNzQv2iRMnoqKiAn//+99RXl4OALj55ptRWFiItDTlk3LHHXdo7dZvV199NSRJ3faNxMREtyXnjEajyw9Od8c8URurJs5XjJZxRbNIXUcozhuMPv3pg3NUCyMwbU1nlvjuT44Lyj3xgtUwJjpnZld7LXUtNlWjqGuxBfz59UcszFG18bE8R0VJxqq3v/KY+lAAsOrtrzBt1ADV2+MjcS2hOmeg/cbDHDVYlV/LBL0Q84shO51eF5FrCcV5A+3Tn/Za26iN9xUX6OuxhHM08PZa2oV6jgp65eenwWCIwt9HL9AyNs0LdgBIS0vDL37xC3+aapKZmQm9Xo+amhqn4zU1NcjKygr5+YkojphnK6Xb3NZhX+2+pBuURVZJRT3ONbWhX5qSvb37Yoql2kiNkop6p2R03ckAqhrbUFJRjwnDMsI3MCIiIopafi3YT548ibVr1+LLL78EAOTm5uKBBx7AsGHDgjq4hIQEXH755Xj33Xcdz7BLkoR3330Xv/zlL4N6LiLqAcyzgREzlGzwzTVAan9g8ESl1robe47VYNXbX3nN+g6wBjepc67J82LdnzgiIiKKf5r3POzZswdmsxklJSUYNWoURo0ahY8//hi5ubkoLi7WPIDm5mZHPXcAqKiowJEjR/DNN98AAJYsWYK//vWvePHFF/Hll19i4cKF+P777x1Z44moB5NEoGIf8MWryt9q6qjr9ErptryblL89LNZL6wQsfrnU5Y6oPev77qMXKmGwBjepoXUnhijJOHCyDm8cOYMDJ+sgSkxIR0RE1NNovsO+dOlSPPzww1i9erXL8UcffRT5+fma+jt48CCuvfZax8f2pHB33XUXNm3ahFtvvRXfffcdfve736G6uhpjxozB7t27XRLREVEPU7bDw/b2NR63t6slSjJeO6Xz+qzxyp1lyDdnORbhrMFNvmjZibH7aJXLXHK3u4OIiIjim+YF+5dffolt27a5HL/33nuxdu1azQOYPHkyfFWW++UvfxnSLfCswx4+rMMeeB+co4BwfBf02+9BZ353B9lSBWy7E+LcjZBHzPS7/49PfudUcqs7+7PGB/5zzinru9oa3O701DmqNj7W5qgnj00bjsUvl7pJfXjh9bc/P4PFL7tmkrfv7nj6ttH4f5cq84512ONrjrIOe3Sel3XY/R9XNOMcDbw967D7L6R12AcNGoSnnnoKN998s9Pxbdu24ZFHHnFsZY9mRUVFKCoqgiiKKC8vZx12olgiS5hybAmSrPUeKp4Drca+KM59ChD8y5R6qFbA5hPut8p3declIi7P5DZl0sZbHfa8vjJWHtajoQNwfcACAGT0TgCWXyaCT1gQERHFppDWYV+wYAHuu+8+fP3115g4cSIApRb6mjVrnGqcR7NFixZh0aJFsFgsSE9PZx32MGId9sD76OlzVKj8EIYj9Z5fB5BirceMkb0hD77ar3OknziHzSeO+IybMulKj3XVteqpc1RtfCzNUV+mA/i1JLvdifFJRT0aPj7opbWAhg6gz6Xj0HjiIOuw+9k+Wuco67BH53lZhz2wa4lWnKOBt2cddv+FtA77448/jrS0NPzpT3/Cb37zGwBATk4OVqxYgQcffFBrd1GBddjDj3XYA++jx87R1jpVYYbWOsDPcVw17CL0TpDR2CF4fdZ4wsX9gp5IrqfOUbXxMTFHVTACuPpS11wsdS02Ve3rW0XowTrsgbaPtjnKOuzRfV7WYfd/XNGMczTw9qzDrp2WsWn+HxEEAQ8//DC+/fZbNDY2orGxEd9++y0WLFiA/fv3a+2OiEibVJUJJ9XGuaHXCZgzRHk2ilnfKZzUZ5JPDPFIiIiIKBoE9LZOWloa0tKUrQYnTpzApEmTgjIoIiKPBk9UssG7fb4XynHTACXODbWlskZnyHj6ttHISndeQGWlJ2Hd/MuYqZtCwp5J3svsRnZ6EsYN7hPOYREREVGEaN4ST0QUUTq9Urpt252Ap1zbBavd1lfXWipram5/TBs1ACUV9TjX1IZ+aUrJLd5Zp1DR6wQsn2XGwi2HPWaS5+4OIiKiniM+Hj4hop7FPBu4ZTNg6rbINuUox93UYd99tAoLtxx2WqwDF0pl7T5a5fZUep2ACcMy8NMxAzBhWAYXShRyBSOzsW7+ZdzdQURERLzDTkRRQBKByv1Ac43y7PngiW7vkDsxzwZGzFDVTpRkrNxZ5jaBnAzlzuXKnWXIN2dxQU5RoWBkNvLNWdzdQURE1MOpXrDv2LHD6+sVFRUBDyZSrFaro3h99799tVMTqybOV4yWcUWzSF1HKM4bjD796SPe5qhwfBf0e5dBaDrrOCan5UCc8gfII2b67mDgVRf+LUrKn24+qah3ubPelQygqrENB/5zDlcO7RuRedpT56ja+J76fXTcD0wAlLKjkmiDJCrH42WOBqPfeJqjNptSJUAWZUhuvpfFEvv4w30doThvoH36015rG7XxvuICfT2WcI4G3l5Lu3DNUVlUbs/YbLao/nmvZWyCLMvuMy51o9P53j0vCAJEUVR98kgpKipCUVERRFFEeXk5tm7dipSUlEgPi6jHyW74FFdUPA3AOYWc/ZvSp0MXo6r3FQGf51CtgM0nfNyxB3DnJSIuz1T1LZGIiIiIyC8tLS0oLCxEY2MjTCaT11jVC/Z4ZLFYkJ6ejtraWscnymq1ori4GPn5+T7r46mNVRPnK0bLuKJZpK4jFOcNRp/+9BE3c1QSYXhmLNB01m1GbBkCYMqBbdFh39vjffikoh7zNxz0Gbfl3nGOO+zhnqc9dY6qjef3UWfxMkeD0W88zdGm1ibse28f+uT2QXJisqpriVaSKKG6tBpZo7PCWuM6FOcNtE9/2mttozbeV1ygr8cSztHA22tpF6452treivPHzmPStZOQlpym+lrCzWKxIDMzU9WCnc+wQylc3/0Hp7tjWtr7G+crRsu4olmkriMU5w1Gn/70EfNztOJjoMs2+O4EyIDlDIxnPwWGBlYycsLF/ZCdnoTqxja3z7ELUBJ6Tbi4n9MzwpGYpz11jqqN5/dRZ/EyR4PRbzzMUYNV+bVM0Asxvxiy0+l1EbmWUJw30D79aa+1jdp4X3GBvh5LOEcDb6+lXajnqKBXfo8zGAxR/bNey9hUfWY//vhj1R22tLTg2LFjquOJqIdqrglunBf2UlmAa/V2lsqiWCZKMg6crMMbR87gwMk6iFKP3TRHREQUl1Qt2O+44w5MnToV//jHP/D999+7jSkrK8OyZcswbNgwHDp0KKiDJKI4lNo/uHE+sFQWxZvSOgGT//QB5v31Yzz48hHM++vHuHrNvzyWKCQiIqLYo2pLfFlZGdatW4ff/va3KCwsxKWXXoqcnBwkJSXh/PnzOH78OJqbm3HjjTdi7969yMvLC/W4iSjWDZ6o1E23VAGeNqqbcpS4IGGpLIoXe47VYEO5DkC70/HqxjYs3HKYb0IRERHFCVULdqPRiAceeAAPPPAADh48iA8//BCVlZVobW3F6NGj8fDDD+Paa69F3759Qz1eIooXOj1QsAbYdieUjeldF+2dC+iC1QEnnOtOrxMwYVhGUPskCidRkvHEW8fdviZD+epZubMM+eYsvhlFREQU4zQnnRs3bhzGjRsXirEQUU9jng3cshnY/Shg6ZKAzpSjLNbNs702FyWZd8upxympqEe1pR2uGRkUMoCqxjaUVNTzzSkiIqIY51eWeJvNhvfffx8nT55EYWEh0tLScPbsWZhMJqSmpgZ7jEQUz8yzgREzgMr9SoK51P7KNngfd9Z3H63Cyp1lqGpscxzLTk/C8llmbgWmuHauqc13kIY4IiIiil6aF+yVlZUoKCjAN998g/b2duTn5yMtLQ1r1qxBe3s7nnvuuVCMM6SsViusVqvj313/9tVOTayaOF8xWsYVzSJ1HaE4bzD69KePuJ2jA6+68G9RUv54sOdYDRa/XOry5Lv9+d2nbxuNqbn+J6uLxDztqXNUbXxUzNEokZGi7kd3Rooh6uZTKPqNpzlqs9kAALIoQ/LyPTAW2Mcf7usIxXkD7dOf9lrbqI33FRfo67GEczTw9lrahWuOyqLy26HNZovqn/daxibIsqypBswNN9yAtLQ0rF+/HhkZGSgtLcUPf/hDvP/++1iwYAFOnDihecDhVlRUhKKiIoiiiPLycmzduhUpKSmRHhZR7JMlZDR/hSRrA9qMvVGXOhwQgltjVJKBlYf1aOgA3G8JltE7AVh+mQjujqd4xK8BIiKi2NbS0oLCwkI0NjbCZDJ5jdW8YM/IyMD+/fsxfPhwpKWlORbsp06dgtlsRktLS0CDDyeLxYL09HTU1tY6PlFWqxXFxcXIz8/3WdBebayaOF8xWsYVzSJ1HaE4bzD69KePaJ2j0rHXIb35ayRb6x3H5LQciFP+AHnETFXXpsYnFfWYv+Ggz7gt947DlUP9S4QZiXnaU+eo2nh+H3X21udn8eA/voAAwV26RqddJqIk42DleZxrake/tESMG9zHr1wPofr8BdpvPM3RptYm7HtvH/rk9kFyYrKqa4lWkiihurQaWaOzoNMH943bcJ830D79aa+1jdp4X3GBvh5LOEcDb6+lXbjmaGt7K84fO49J105CWnKa6msJN4vFgszMTFULds1b4iVJgiiKLse//fZbpKVF7yfFG6PR6PKD090xLe39jfMVo2Vc0SxS1xGK8wajT3/6iKo5WrYD8usL0L08m9BUBcP2e5TEcj4SyKlV12JTHReJ/5dA9dQ5qjae30cV00floLS0FG9Vp3QmoFNkdcvjEIpcD6H6/AXabzzMUYNV+bVM0Asxvxiy0+l1EbmWUJw30D79aa+1jdp4X3GBvh5LOEcDb6+lXajnqKBX3pA2GAxR/bNey9g0L9inTJmCtWvX4vnnnwcACIKA5uZmLF++HNOnT9faHRHFOklUsrxDdrM5t7PI1O6lSmI5D4nktGR775eWpGpYauOIYtXoDBm/vv0afPZtk9uvnd1Hq7Bwy2GPuR5Yq52IiCj6aV6wP/nkkygoKIDZbEZbWxsKCwtx4sQJZGZm4qWXXgrFGIkomlXuByxnPRSYAgAZsJxR4oZOcnlV6x3A8UP7Ijs9CdWNbS4LEUDZEpyVrixciOKdXie4Ld0mSjJW7ixz+zXCWu1ERESxQ/Oeh0GDBqG0tBSPPfYYHn74YYwdOxarV6/GZ599hn79+oVijEQUzZpr/I6z3wHsulgHLtwB3H20yqWNXidg+SwzANd0W/aPl88ycxFCPVpJRb3L11VXXWu1ExERUfTSdIfdarVixIgR2LVrF26//XbcfvvtoRoXEcWKVJXl07rFBXIHsGBkNtbNv8zlznz353eJeirWaiciIooPmhbsRqMRbW384U5EXQyeCJhyIFuquuWrthMAU44S14WWO4DutvwWjMxGvjlL9bPvRD0Jcz0QERHFB81b4hctWoQ1a9bAZlOXqZmI4pxODxSsAdA9Rzzg2KResNol4Vww7gDan9/96ZgBmDAsg4t1ok72XA+eviIEKLkimOuBiIgoumlOOvfpp5/i3Xffxd69e5GXl4devXo5vf7aa68FbXBEFCPMsyHO3QjrjiVOddhhylEW625KuvEOIFHo2HM9LNxyGALgtlY7cz0QERFFP80L9t69e2Pu3LmhGAsRxTB5xEzsPQnMGNkbhtY65Zn1wRM9lnJjtnei0GKuByIiotinecG+cePGUIwjoqxWK6xWq+PfXf/21U5NrJo4XzFaxhXNInUdoThvMPr0p4+onqOCDh05V0I2GpWDoqT88eCxacOx+OVSj3cAH5s2HJJogyR6HX7QRWKe9tQ5qjae30edqb2W64ZnYvIlk3Cw8jzONbWjX1oixg3uA71OcGorSrLbGH/OGaprCWb7aJ2j9scNZVGG5OV7Zyywjz/c1xGK8wbapz/ttbZRG+8rLtDXYwnnaODttbQL1xyVReU3SpvNFtU/77WMTZBl2d3NrbhWVFSEoqIiiKKI8vJybN26FSkpKZEeFlGPVFon4LVTOjR0XFgc9E6QMWeIhNEZPe7bE1HY8WuQiIgovFpaWlBYWIjGxkaYTCavsZoX7EOHDoUgeH7m7euvv9bSXURZLBakp6ejtrbW8YmyWq0oLi5Gfn4+jPa7hB6ojVUT5ytGy7iiWaSuIxTnDUaf/vQRsjm6dw+mjkiDoa0WSO0PedAEp+3soZyjau7uhVMk5mlPnaNq4/l91FmwrmXPsRosfrnU5bEU+1ff07eNxtTc/kE9Z3eB9htPc7SptQn73tuHPrl9kJyYrOpaopUkSqgurUbW6Czo9JpzHEfVeQPt05/2WtuojfcVF+jrsYRzNPD2WtqFa462trfi/LHzmHTtJKQlp6m+lnCzWCzIzMxUtWDXvCX+oYcecvrYarXis88+w+7du/Hf//3fWruLCkaj0eUHp7tjWtr7G+crRsu4olmkriMU5w1Gn/70Ecw5KhzfhSnHliDpSPeEcWtcEsaFYo4aAVx9qcp67mEUiXnaU+eo2nh+H3UWyLWIkoxVb3/lNoeEDGXRvurtrzBt1ACnN9BC9fkLtN94mKMGq/JrmaAXYn4xZKfT6yJyLaE4b6B9+tNeaxu18b7iAn09lnCOBt5eS7tQz1FBr/y8MhgMUf2zXsvYNC/YH3zwQbfHi4qKcPDgQa3dEVGkle2Afvs90Hf/td1SBWy7E7hls9ss70QU20oq6p2S0XUnA6hqbENJRT0mDMsI38CIiIjIIWhv60ybNg3bt28PVndEFA6SCOx+FIDspl5z5wJ+91KEPesbEYXcuSbPi3V/4oiIiCj4grZgf/XVV9G3L8svEcWUyv2A5aybxbqdDFjOKHFEFFf6pSUFNY6IiIiCT/OW+LFjxzolnZNlGdXV1U5APsoAACAASURBVPjuu+/w7LPPBnVwRBRizTXBjSOimDF+aF9kpyehurHN7XPsApSa7eOH8s14IiKiSNG8YL/hhhucPtbpdLjoooswefJkjBgxImgDI6IwSFWZ6E1tHBHFDL1OwPJZZizcchgC4LRot78tv3yWOaIVG4iIiHo6zQv25cuXh2IcRBQJgycCphzIlioInu6xmXKUOFEK+/CIKLQKRmZj3fzLsHJnmVMCuqz0JCyfZUbByOwIjo6IiIg0L9gPHz4Mo9GIvLw8AMAbb7yBjRs3wmw2Y8WKFUhISAj6IIkoRHR6pXTbtjsdZZwu6PyoYLUSp2LBLsnAJxX1qGuxoV+aspWWd+eIolvByGzkm7NQUlGPc01t/NolIiKKIpoX7Pfffz+WLl2KvLw8fP3117j11lsxZ84c/OMf/0BLSwvWrl0binESUaiYZ0OcuxHWHUuQbO1eh3216pJue47VYOVhPRo+vlDeMZt36Yhigl4nsHQbERFRFNK8YC8vL8eYMWMAAP/4xz/wk5/8BFu3bsVHH32E2267jQt2ohgkj5iJvSeBGSN7w9BapzyzPniicmddhd1Hq7D45VKXTfXVjW1YuOUw1s2/jIt2IiIiIiKNNC/YZVmGJClbY9955x3MnDkTADBo0CDU1tYGd3REFD6CDvLgqwGjUVMzUZKxcmdZ52LdeQutfZv9yp1lyDdncYstEREREZEGmhfs48aNwxNPPIHrr78e//73v7Fu3ToAQEVFBfr3j81M0larFVar1fHvrn/7aqcmVk2crxgt44pmkbqOUJw3GH3600e0zdFPKuqdklV1JwOoamzDgf+cw5UxUh4qEvO0p85RtfH8Puos3NciSjI+PvkdDtUKSD9xDlcNuyhob8AFei3xNEdtNhsAQBZlSDGe6NM+/nBfRyjOG2if/rTX2kZtvK+4QF+PJZyjgbfX0i5cc1QWlVtINpstqn/eaxmbIMuyu9TQHn3++ee4/fbb8c0332DJkiWOrPGLFy9GXV0dtm7dqm20EVBUVISioiKIoojy8nJs3boVKSkpkR4WUUw6VCtg8wnfW+fvvETE5Zmavt0QURQorRPw2ikdGjouLNB7J8iYM0TC6Ax+TRMREWnV0tKCwsJCNDY2wmQyeY3VvGD3pK2tDXq9HkaN22kjyWKxID09HbW1tY5PlNVqRXFxMfLz831ei9pYNXG+YrSMK5pF6jpCcd5g9OlPHy5tJBHC6QNAcw2Q2h/yoAmOZ8/DMUc/qajH/A0H3bbpasu942LqDnu452lcz9EgxPP7qLNwXcueYzVu81PYl+5P3zYaU3MD210X6LXE0xxtam3Cvvf2oU9uHyQnJqu6lmgliRKqS6uRNToLOr0ups8baJ/+tNfaRm28r7hAX48lnKOBt9fSLlxztLW9FeePncekaychLTlN9bWEm8ViQWZmpqoFu+Yt8adPn4YgCBg4cCAAoKSkBFu3boXZbMZ9993n34gjzGg0uvzgdHdMS3t/43zFaBlXNIvUdYTivMHo058+jEYjjCfeBnY/CljOXnjBlKOUauuS3T2Uc3TCxf2QnZ6E6sY2T5XckZWehAkX94u5Z9gjMU/jbo5qaBOM75FqYuLl+ygQ2msRJRmr3v7K7de1PT/Fqre/wrRRA4LytR3otcTDHDVYlV/LBL0Q84shO51eF5FrCcV5A+3Tn/Za26iN9xUX6OuxhHM08PZa2oV6jgp65eeRwWCI6p/1Wsam+X+ksLAQ7733HgCguroa+fn5KCkpwWOPPYbf//73WrsjIj8Jx3cB2+50XqwDgKVKOV62Iyzj0OsELJ9l7vzI+Vd7+6/wy2eZY26xTtTTlajMT1FSUe8xhoiIiAKjecF+9OhRjB8/HgCwbds2jBw5Evv378ff//53bNq0KdjjIyJ3ZAn6vcvQfYHc+aLy1+6lgCSGZTgFI7Px9G2j0TvB+XhWehJLuhHFqHNNnhfr/sQRERGRdpq3xFutViQmJgJQyrrNnq1sux0xYgSqqqqCOzoiciuj+SsITWe9RMiA5YzybHuYTM3tD+spEReZr0Jdiw390pIwfmhf3lknilH90pKCGkdERETaab7Dnpubi+eeew779u1DcXExCgoKAABnz55FRkZG0AdIRK6SrA3qAptrQjuQbnQCcOXQvvjpmAGYMCyDi3WiGDZ+aF9kpyfB01exACA7XXljjoiIiEJD84J9zZo1+Mtf/oLJkydj3rx5GD16NABgx44djq3yRBRabcbe6gJT1WdvlmQl4/sbR87gwMk6iBLLNRH1ZF3zU3RftDM/BRERUXho3hI/efJk1NbWwmKxoE+fPo7j9913H2uZE4VJXepwyGk5EJqq4P45dgEw5Sgl3o7t8dnfnmM1WHlYj4aPL5Rny05PwvJZZj5/TtSDFYzMxrr5l2HlzjKnBHRZ/P5AREQUFpoX7AAgyzIOHTqEkydPorCwEGlpaUhISOCCnShcBB3EKX+AYfs9UO51dV20d97tKljtqMfuze6jVW7rLFc3tmHhlsNMGkfUwxWMzEa+OQsH/nMOe/d9gimTrozJMo1ERESxSPOW+MrKSuTl5eGnP/0pFi1ahO+++w6AslX+kUceCfoAicg9ecRM4JbNgKnbYtqUoxzvUofdE1GSsXJnWedi3fmXb/sCfuXOMm6PJ+rh9DoBVw7ti8szZVzJZJJERERho/kO+4MPPohx48ahtLTUKcncjTfeiAULFgR1cETkg3k2MGIGULlfSTCX2h8YPFHVnXVAW53lcT8wBWnQRERERESkhuYF+759+7B//34kJDgXXB4yZAjOnDkTtIERkUo6PTB0kl9NtdVZ5oKdiIiIiCicNG+JlyQJoii6HP/222+RlpYWlEERUXiwzjIRERERUfTSvGCfMmUK1q5d6/hYEAQ0Nzdj+fLlmD59elAHR0ShxTrLRBQsoiTjwMk6loYkIiIKIs1b4p988kkUFBTAbDajra0NhYWFOHHiBDIzM/HSSy+FYoxEFCL2OssLtxyG8sT6haV79zrLkuvGGiIiAEq1ie6l31gakoiIKHCaF+yDBg1CaWkpXnnlFZSWlqK5uRk/+9nPcPvttyM5OTkUYww5q9UKq9Xq+HfXv321UxOrJs5XjJZxRbNIXUcozutXn5II4fQBR4I4a9Y4zX0Ee45eNzwTa28eieVvfIGGjgvHs9IT8di0EbhueKbbr5FAxhXtInEtUTNHg9CH1jbB+B6pJoZzNDTn3HOsxmtpyKdvG42puf019xvouILZJlxz1GazAQBkUYYkSqrGFq3s4w/3dYTivIH26U97rW3UxvuKC/T1WMI5Gnh7Le3CNUdlUflpZLPZovrnvZaxCbIsq96zZrVaMWLECOzatQs/+tGP/BpcNCgqKkJRURFEUUR5eTm2bt3KGvIUctkNnyLv278j2VrvONZq7IsvBt6Oqt5XRHBkCkkGTloEWKyAyQgMM8lg5SYi8kaSgZWH9Z1v9rn7hiGjdwKw/DKR30+IiIg6tbS0oLCwEI2NjTCZvCd21rRgB4ABAwbgnXfeiekFu53FYkF6ejpqa2sdnyir1Yri4mLk5+fDaDR6ba82Vk2crxgt44pmkbqOUJxXS5/C8V3Qb78HgOz0K63ykYyOG16ALveGoJ+Xc9Q/kbiWSM/RYPahtU0w5p+aGM7R4J/zk4p6zN9w0GfbLfeOw5UecmEEei3xNEebWpuw77196JPbB8mJsblr0U4SJVSXViNrdBZ0es0pk6LqvIH26U97rW3UxvuKC/T1WMI5Gnh7Le3CNUdb21tx/th5TLp2EtKSozchusViQWZmpqoFu+Yt8YsWLcKaNWvwwgsvwGDQ3DwqGY1Glx+c7o5pae9vnK8YLeOKZpG6jlCc12efkggULwNcNowCAmTIABL+tRzCqDmq66erOq8fsZyjziJxLRGZoyHqQ2ubYMw/NTGco8E7Z12LTVWbuhYbjEYjRElGSUU9zjW1oV+aktDSPvpAryUe5qjBqvxeJeiFmF8M2en0uohcSyjOG2if/rTX2kZtvK+4QF+PJZyjgbfX0i7Uc1TQK7fGDAZDVP+s1zI2zSvuTz/9FO+++y727t2LvLw89OrVy+n11157TWuXRPGtcj9gOevxZQEALGeUOD/rqRMRRYKW0pCeEtM9Nm14qIZHREQU8zQv2Hv37o25c+eGYixE8am5JrhxRERRwl4asrqxzc0eIuUNyaz0JJz/vgOLth52m5hu8culuOdSASwMS0RE5Erzgn3jxo2hGAdR/Er1nB3ZrzgioijRtTSkkpHjAnu+jsdn/Aj/82aZ2wW9vZjka6d0+LUkI3o3LxIREUWG6ocUJEnCmjVr8OMf/xhXXHEFli5ditbW1lCOjSg+DJ4ImHLgPoOy8gurbBqgxBERxZiCkdlYN/8yZKU7b4/PSk/CuvmXoU+vRKdt8N3JABo6BBysPB/ikRIREcUe1XfYV61ahRUrVuD6669HcnIy/vznP+PcuXPYsGFDKMdHFPt0eqBgDbDtTqDbPSh7lngxfxUMGhLOdeUuiZOe9ZOIKIwKRmYj35zl9nvRG0fOqOrjXFN7iEdJREQUe1Qv2Ddv3oxnn30W999/PwDgnXfewYwZM/DCCy9Ap4uPLJFEIWOeDdyyGdj9qHMCOlMOPs2Yg7EjZvrVrackTstnmVEwMjvQURMRqabXCZgwLMPluPrEdInBHhIREVHMU71g/+abbzB9+oWUMNdffz0EQcDZs2cxcODAkAyOKK6YZwMjZijZ4JtrgNT+sOVcgardezDWj+72HKvB4pdL3SZxWrjlMNbNvwzXDc8MxsiJiPymJjFdeoKMcYP7hHtoREREUU/1rXGbzYakJOd3yY1GI6xWa9AHRRS3dHqldFveTcrffm6Dl2TgibeOe0ziBAArd5ZBlNxFEBGFjz0xHeCaycP+8ZwhEh/lISIickP1HXZZlnH33XcjMfHClrW2tjb84he/cKrFzjrsRKF30iKg2uL5eU8ZQFVjG5M4EVFUsCem6/4IT1ZnHXax8lAER0dERBS9VC/Y77rrLpdj8+fPD+pgiEgdi8qNLeea2uHfPXwiouDylJhOEm14q/JCHBNpEhERXaB6wc7660TRw6SyWHG/tETUhXYoRESquUtMJ4kX/s1EmkRERM6Y3p0oBg0zycgyJXqo7K48F5qdnsQkTkQUM/Ycq8HCLYddarbbE2nuPloVoZERERFFDhfsRDFIJwC/nT4CgOckTstnmbmNlIhiAhNpEhERuccFO1GMmprbH+vmX4asdOfqDVnpSVg3/zJuHyWimKE2kWZJRT0A5Tn3Ayfr8MaRM/ikoh5cxxMRUbxS/Qw7UVyTRKf66Bg80e+Sa+HkKYkT76wTUSxRn0izze1z7r0T9DAOqcHMMQNDNEIiIqLI4IKdqGwHsPtRwHL2wjFTDlCwBjDPjty4VHKXxImIKJaoTaR5qrYFa98pd9k639ABLH65FAaDnruLiIgornBLPPVsZTuAbXc6L9YBwFKlHC/bEZlxERH1IGoSaWaZEvFSyTdun3O3Z+/gc+5ERBRvuGCnnksSlTvr3tIc7V7qXHOIiIiCTk0izXnjf4BqSxs86f6cOxERUTzglngAVqsVVqvV8e+uf/tqpyZWTZyvGC3jimaRug535xUqP4Sh+511JzJgOQPb1x9AHny1qj6DMa5gtuEc9U8kriUU54yFOao2nnPUWbzM0a79/b9L++Lp20bjibeOOyWgy0pPxGPTRqBDlFT1V9XwPaxWk6pzRtsctdlsAABZlCGpvN5oZR9/uK8jFOcNtE9/2mttozbeV1ygr8cSztHA22tpF645KovKTTebzRbVP++1jE2QZbnH7R0rKipCUVERRFFEeXk5tm7dipSUlEgPi8JsQP0BjKtc5zPu4OCFONN3QhhGREREkqxkjbdYlWfbh5lk6ATgRKOAZ8p8JwP9pVnEJek97lcbIiKKIS0tLSgsLERjYyNMJu9vMvfIBbudxWJBeno6amtrHZ8oq9WK4uJi5Ofnw2j0ngVHbayaOF8xWsYVzSJ1He7OK1R+CMOWG3y2tc1/3eMd9kCvxZ8+OEdDLxLXEopzxsIcVRvPOeosXuaoln5FScbkP32AGku72weZBCh3499bco3PShnROkebWpuw77196JPbB8mJyT7HFc0kUUJ1aTWyRmdBpw/fE5ihOG+gffrTXmsbtfG+4gJ9PZZwjgbeXku7cM3R1vZWnD92HpOunYS05DTV1xJuFosFmZmZqhbs3BIPwGg0uvzgdHdMS3t/43zFaBlXNIvUdTid94fXKNngLVVw/xy7AJhyYPjhNV5LvAXjWvzpg3M09CJxLaE4ZyzMUbXxnKPO4mWOqunXCGDF7Fws3HIYArp/15YBCFg+KxdJiQlBO6c/8YHEGKzKr2WCXoj5xZCdTq+LyLWE4ryB9ulPe61t1Mb7igv09VjCORp4ey3tQj1HBb3yhq3BYIjqn/VaxhYfX2lE/tDpldJtADymOSpYHdR67KIk48DJOrxx5AwOnKxjNmMiIg0KRmZj3fzLkJWe5HS8dwLw9G2jWdKNiIjiDu+wU89mng3cstlDHfbVQa3DvvtoFVbuLENV44Usx1mmREzPEjA9aGchIopvBSOzkW/OQklFPc41tSEjxYDvyj7G1Nz+kR4aERFR0HHBTmSeDYyYAVTuB5prgNT+wOCJQb2zvvtoFRZuOeyy8b7G0o4NFh0uO1aDmWMGBu18RETxTK8TMGFYBgDlufC3vozwgIiIiEKEC3YiQFmcD50Ukq5FScbKnWXeqr1j1dvHMW3UAJ+JkoiIiIiIqOfgM+xEIVZSUe+0Dd6VgKrGdpRU1IdtTEREREREFP14h50oxM41eVusa48jIiLfREl2POfeLy0J44f25S4mIiKKOVywE4VYv7Qk30Ea4oiIyDt3ST6z05OwfJY5Ipnk7W8eVDV8j68bBYiSjOgtNkRERNGEC3aiEBs/tC+y05NQ3djm9jl2QEZ2unL3h4iIAvP/27vz6CiqtH/g3+rOBulsJJIV2UKAEEwAE0AJosOqAy6o7zivqKAe5TA6jprxdcYjOGccmaPOGRdkHCSKx+WH27hCGEfZRBAwgoTIHqJmY0xIOgtZ6K7fH203dNJddTuprq5Ofz/ncJTu21W3Og+dfu69dR9vm3zWNrVj6WulWH3zRPxidBLsMvBVRQPq2856nYHXItHuOXhgxjtPb8OKBeNYho6IiFQxYSfyM7NJwvL52Vj6WikkwO1LpPPvf5w3hks1iYj6SG2TTwnAYx+Vo7MrC4+VmtG4a6/r+e4z8Fok2koVQpyDB0zaiYhICTedI9LB3JxUrL55IlLi3Je9p8RFYkmWnfWDiYg0oLbJpwygpqkd967/Fo2d7s85Z+BLympciXb3YzkT7ZKyGtW+iFQIeeyjctjsntdeERERAZxhJ9LN3JxUzMpOcdsEaUJGDDaVbAx014iI+gXfNu90X9XknIFf8eFBAJLqLP2s7BTFlVGigwe7KxpcNeWJiIi6Y8JOpCOzSXL7YtbV1RXA3hAR9S993bxTBlBr7VBtI5JoB6JCiM0u47vqTpxutSMh2oSxaRG83YqIKMgxYSciIqJ+QX2TT+2oJdp6VwjZdawdxdusqG+xux5LtJiwZHospmSyCkko4cANUf/ChJ0Cx24DKr8EWuoASzIw9BLAZA50r4iIKEiJbPKpFbVEW23wQAKQolGFkF3H2vHkhsYej9e32PHkhkYUXRnPpD1EfHW8HS9/0cKBG6J+hJvOUUBIhz4G/p4DrPsl8O7tjv/+PQco/zDQXSMioiDmfZPPKLzw6wlIjYuCt7lGCUBKbCRSYpXbiJTidA4eOF/T/RgAsHx+dp9nPm12GcXbrIptirdZubldCNhfL+GpEvdVFsC5gZtdx7S7/YKI9MMZdtJdauMemN99Hj3mOqw1wFu3ADe+CmQvCEjfiIgo+Hna5NNZZ91kkrD0tVKc20LOwfl/KxaMAwCvs/SAeKLtHDxwLw/nqBCyfL42ddi/q+7skaB1V99ix3fVncjJiOzz+ciYbHYZ751Unocr3mbFpKHc4JAo2DBhJ33ZbRj/4+vwvDDx5y9PJf8HjLmKy+OJiKjXum/y6TQ3JxXP/SoXj7y3z620W0q3OuyiibbNLjt2hG9sxYkmCTa7jPBu53MOHtQ0tuLEwX34zf9MR1RkhCbXebpVOVn3tR0Fp0M1XWjsVB5Eqm+x41BNFxJ06hMRaYMJO+lK+mEnBnQ1KLSQAWuV49724YW69YuIiELHnHHJ6DppwwXZU1DfdtZtBt5JJNEuKavpltSb8c7T27BigXtS7xw86OqKxYYfv9F0A7CEaLG7G0XbUXDyZeCGCbuxcdNA6o4JO+mrpU7bdkRERL1gkoDJwwchPDzcaxulRLukrAZLXyvtsV6sztqBpa+VYvXNEzVZ8q5mbFoEEi0mxWXxiRbHl37qv3wauGnxc2eo11jtgTzhcCvpy5KsbTsiIiKd2ewyHvuo3OvNXQDw2Eflumz0ZjZJWDI9VrHNkumxms7Q2ewyyn7swPbDZ1D2Ywc3tDOAManhiI9Q/jkkWkwYk+p9gIoCy1ntgZsGUnecYSddyUOm4kz4IER1nYbkrdBNbJqjxBsREZEB7a5ocLu3vTsZQE1TO3ZXNHi8j15rUzKjUHRlvC4zc/1xBrA/LEE2myRcN8yO4iPe9//ReuCGtCNa7SF/RCR/hiGICTvpy2TGgYz/RX7F8+hZFffnD6C5K7nhHBERGdapZrGZLtF2WpiSGYX8EZF+TTz7Y733/jQAkZso48G5sYp12O02bj5oRKFS7cEuAwerOtF4Rg7awbFAYMJOuquJz4dt4csI+/QPgLX63BOxaY5knSXdiIjIwAbHiCVyou1EiMwCm02S377M+zoDaLPLONok4cSRdgyKMffor8j1+Hvmuz8OQEweGYWCzAFBv2Ig1Pij2oPRVo58dbwdL5Wa0dh57t9csA6O6Y0JOwWEPOaXwLgFjt3gW+oc96wPvYQz60REZHgFwwchNS4KtU3t3m7uQkqcY+d5Ec7ScJX1p/Fjk4T8bveEG2EW2JcZwJZ2GcVbrahvNQPljiT//P6KXI+/r9mXAYhgS3X9OXBD/qF1tQc9PjNsdhnlVZ048ZOE01WdyM7wvlx/17F2PFXS899bMA+O6YkJOwWOyczSbUREFHTMJgnL52dj6Wul3m7uwvL52UKzWZ5Kww2qbMHtl5ldya0RZoFFZ/b2nOjAx/vaejzu7O/VEwfig1LvzxddGQ8Afr9mXwYgsnXcqM05K9rQbIOtSUKyXQ7auQyjzfAamZbVHvT4zHAfEDADRxu9Dgi4D455/vnz/nxlTNiJcG5241Rzu8d6vEREROebm5OK1TdP7JZsAylxkVg+f5xQSTdvpeEaWmU8uaERD8yLwyvbmxWPodcXXdGZvW2Hzyg+/9E3PZP1863d2gRJUr4WLa7ZH0uQ+6rnrKgZb1bWY8llwbdk2AirQrTmzwEIZ7UHT4m2k8imgXpsXufrgECo3J/vT0zYAXR1daGrq8v1/+f/V+11Im1F2qm18aVfRhao61A676aDdfjzhkOotXa4HkuJjcQjV47BnHHey8tpcS29OQZj1P8CcS3+OGcwxKhoe8aou/4So1ocN5Ax+ovRSZgxqhB7K0+jprENlYcO4K7rpiIqMsLjsc8/js0uY8WHBz0uqXdas9kKa7tyqa76FjvKf+zAuHT1mTebXcahmi5XwjEmNVz4S/vo5DAkRptQr5DAxkZJsJ5R7q9aBbiGVhlQfFeUr9m5qZra5mrxA8SuO36AJHxMb5yvO9tlw+GqTo/v/1fHvSwZbnUkQQ/OjcXkkd4TXdE+qrVT66vIebxeS4vYtXTXl7hV4svP9avj7Xh5e4tb/CdGm7C40OLTtSidt2B4hGPTwO7nsZiweJoFBcMjVH9u5T92CCXH3f/9eOtT9/c+KzkMxVvVBwQmDT33M2potim2d2potsFuswvHqLfnZZvj8+Ps2bOG/n3vS98kWZZDrnjmqlWrsGrVKthsNhw5cgRvvPEGBg4cGOhuUQDsr5dQfMQ5a3D+h7/jn8WSLDtyE0PunwgREfnZ0SYJz5drs9b5llE2TEpS/l21v17CeydNaOw897suPkLGdcPEf8+p/c68LEXG1lqxmfi+ErlmJXYZeKzUjMZOwPMyXRnxEcDyiTZoMYmq9P6PHyTr2pe+9FUtVrR+X7WI277y5buiXQaOWyVYu4DYcGBkrOzzz6wvx/j6JwmvHlX/XOntZ0Z0mIzWs+qd+U22DaPiHMcX/aw7/zWhoK2tDb/+9a/R1NSE2NhYxbYhOcO+bNkyLFu2DFarFXFxcZg9e7brjerq6sKnn36KWbNmITxc+Z4l0bYi7dTa+NIvIwvUdXg6r80u44mntwHo8PAKCRKAjXUD8fv/ne5xJFeLa+nNMRij/heIa/HHOYMhRkXbM0bd9ZcY1eK4wRqjtu9+AsoPCPVXzYhxiUhTmGH/6ng7inf2nBVr7JRQfMTsNuOpNJuZBmDQCA8zjRYzFk+zwBJlwtb3vS/p1ZLzmrv3d/RgM04dqENKbgpMZuXBgzviPc8EO0i444pYZIx0lEKr3V8rdExPdh498/P77/5dwvn+35g/EI2dSrcKSGjsBJpSkryupBDto1o7tb7ePzsGQ5tPe339wapOt13Ae3MtTqJxKzID76mNJMtu74WnNgDwwav1ADzN5jrO8UF1OGb/IhF7KzqEZuFFflYZiu9MT85jDhuTABxVngEHen5mdO+Tt/deJFkHAHN6AtKyHNecbJfxZmW94uqcRIsJ0y67wLWKQ+n9UXv+TMcZnD54GoWXFyJmQIxQfwPBalX/OTmFZMLeXXh4eI9frp4e8+X1vW2n1saXfhlZoK7j/PPuPV7vCNUl0gAAHUFJREFUtgy+OxlATVMHvvmxGVNHJgodU4t++eM1jNHeCcS1+OOcwRCjou0Zo+76S4xqcdxgi9HU+Gihc8YOUF5mnmgxITsjEiYvU3A2u4yXv2hRPMfLX7SgIHMA9pzoUL3veGrWQK9lw2x2WXXjLJOkvCx+ULQESZJUN9/KzojEbk/9jTbh6nQJaWaTanI9NWsgikwm4XutTQLH7M5ml7FuR6timw3fKt/379R4RlY9v2gfPbUT6eu6L1vxSI738zSq3BJxfjtnkuwtlkTiVpYkvLK9uVcVBxZPs2AIHNeyu6LTY5uZ4wYoJpqAY4n5v0rbsP6rnu9dfasdT5VYUXSlSZN4UpOdESm0eZ23zwyT2QRZklTfezWDYsyuazOZgSWXOe/Pl+Fp5cWS6bEID3efhVd7f7w9L5kdxw8LCzP073pf+saEnULWqeZ29UY+tCMiIhKlVhoOcHyxvq0wBk9vbPJ6HLWNqEQ3fHp3T4vnhMPDRlLeyoaJbJw1f4LnXeKdbr8sDoDnXeKdlkyPxZ4THZ43vmq1o/iICYNGtGNq1kDVjcKmZEYhf0Sk6mZidvnn2eMzsk8bjn1X3flzwue9bUuHWJIruvFfb4n0tb7FjuNWyesMsC/lyZQ2prNEKQ/aOPvi6d+GaMWBp0qsWJIl4QeFe+49/Zvw5BMPlRHOp9fmkFpsXifymaHE0272UzKj8ODcWLz0edPPt0ucaxvMGxHqhQk7hazBMWIfDqLtiIiIRCmVhnNyfpE1SVKvd9wW3eVcq4RjSmYUiq6M/7kOu+f+ZqVEqF5P0ZXxXtvkj4jE0lf+q9gP0dlXQL1u+VfH2/FSqdltqbfW778lUlJM3EVLevWFaF+tCntliZYns55RTrZ/mdf3vaVEKg68W2GCuapvs8mA+qCLnrugu/4N+vkzwxtvAwKTR0YhvbEBTSlJPg98hTom7BSy1GY3JAApcY4Sb0RERFrzVhpuULSE2y+Lc32xFp0F9kR0xlPLhGNKZhQmDQ3HF1trYU5PwKAYs1t/Ra5HqU2Z6k7YktDsq8is3q5jyjueO4/jbSZf9P2/Km+g4myuSEmvvhLta+zPK3m9XbPaDO9thTGq5Qq3HRK7TUCJSMWBpi4J6Opbgqo22OKkZ4lAPT4zut+uIzIgYJKAcekRmt8K0N8xYaeQpTS74fw4Wz4/myN/RETkN3NzUjErOwW7KxpQWX8aPx4qR/6l8bAMcP/SqzYL7I3IjKc/Eg6zScKoOBlpWVEev5yLXI+3NlokPiIrBtxrWntuV7zNCrsse53Jzx8R+XM5PJvXYyRaTFiYb8GFieGqs6L+rAU+Ni1CqK8jY886ypx90eK1r0ozvCLL3a3tsur+DUahNtji5O9bGrrz52dGosWEVbck4VB1J04crMeIcYnIzvD/kv9QxYSdQpq32Y2UuCgsn5+NuTmpAewdERGFArNJwtSRiRiXGo7NNQe9biDX22OrzXgaNeHwRot+iKwYEL3/X20mf3GhBU+VqO9DcP6saEOzDbaq05h22QWuzbiU7vkuGN735fJmk6Ta18XTLDhw4jSKd6mvOvA2w7v9sNjs+fTRA/Cxyq0aevmfydH4z8EzXgdluj/XnR63NGhF9D748DATxqVHIKFORlp6hKafW+SOCTuFvPNnN041t2NwjGMZPEcJiYioP1Cb8Qy2hEN9BtDzTtTdqc3UazWTv2pRIpZk2fFBVbjX+/qdnLOidpsd1S2y67vIrmPtXjdPe3JDIx6cG4shfe6t4z7jJVmnvfZ10tBw3P258oDJ+asXPA2IiA645I+IxNg0z/sdOJfVK8WsSMWBuHAZ5ggzGlRKji3Mt2BhvsXr6oa+bvRmNH29D560xYSdCOdmN4iIiPojtXtagynhEJkBFKGWOGo1k3+opgu5iTJm/yIRh+vO+ryc3X1pvmcvf9GCR3L63F0AUOzrge/b0dip3Ge11QuiS66d5/QWtyZJUowBkYoDC4fbMWhEnMd9CpzOj31v19QfE9y+3AdP2mLCTkRERBQClO5pDbaEQ6m/81O78FF1uGL9bJEVAyKJpYjTrXYkoPf3FIsuzVcqt+arvu4foNTO19Jj3voiGrPe2iyeZsGQpgakjYxC0ZWmPsd+f0xwexuzpC0m7EREREQUdAmHp/6OTg5D3b4aJI20CM+aeuOeWIots/ckIdoE9KFymBbl1rTiS511JVoNEPWl4oAky6guFT+OCJEE12aXcbRJwokj7T0qKBB5woQ9GNhtkCq/QHrDTkiVscCI6YDJHOheERERUT8TbDNq3ftrtzmSv8kazpo+ODcWL33ehMbOc4+L3kedaDFhTGo46up8vLDz+FpuzZ/GpIYjPkJWXBYvut+BnkmypzZ2m6zaRmu7jrWjeKsV9a1moNwxoNSbVSzdqwWMTmZK15/xp2t05R8CJQ8hzFqNiwGgcjUQmwbM/SuQvSDQvSMiIiIyJK0Swskjo5De2ICmlCQ0npF9uo9ai3v/Re/5Hhl7tk/nEWE2SbhumB3FR7xPHPlyzcE2QNQXahsHOnfXFzlOj4GoaBOuTpeQ5kN/zk/64wdIiDN+Bb2QxYTdyMo/BN66Be4VwgFYaxyP3/gqk3YiIiIiL7RKCE0SMC49okdNeZGl3c5Z/94Sued78TQLTE0Nwse02WWU13T0aiAjN1HGg3NjFeuwkzuRjQPP313fG69Jf6sdxUdMGDSiHVOzBqr2x1PSHx9hxh3xYq8nfTFhNyq7DSh5CD2SdQCu+6hK/g8YcxWXxxMREREFiB73/qsNDBQMj3Ddj61mf72ED16t91i2TbSe++SRUSjIHBA0+x0EmujGgUq764tWCyjIHNCrpL+xE3iqxIoik4mDLgbDhN2oKr8ErNUKDWTAWuVoN7xQt24RERERkTs9lnYrDQyIzuJ/dbwdxUdMANzb96aeeygtZ+8rLXbXV0/6pT4m/Y4kX2Smn/TV9wKT5B8tgruTiLYjIiIioqDmTJILRw9AToZvSZXNLuPl7crb1b/8RQvsvJdZc1rsrq9P0n9upp+Mgwm7UVmStW1HRERERCHru+rOn5fBe0/ynfXcSVvOjQOVqO2ub5Skn/THhN2ohl7i2A3e64eqBMSmO9oRERERESkwUj33UOPcOFCJ2u766km/rEvST/rjT8OoTGZH6TYAPZP2n/8+dyU3nCMiIiIiVUaq5x6KnBsHJnb7OSRaTEIl3USS/sXTLH1M+tVn+kl/3HTOyLIXOEq3lTzkvgFdbJojWWdJNyIiIiISMDYtAonRJtS32uBtBade9dxD1ZTMKEwaGo4vttbCnJ6AQTFmn3bXV6oWcHVaFyaPFEv6PZcIdFShUpvpJ/0xYTe67AXAmKtw9sQ27Nu+CXmFcxA2Yjpn1omIiIhImNkkYXGhBU+VNHlt42s9d/Kd2SRhVJyMtKwomMy+L3b2VC1gdHIY6vbVCL/eU9IfHwHccUWsJiXdbHa5RzUDDgH0HhP2YGAyQx46DVUHrcgdOo3JOhERERH5bPLIKCzJOo0PqsK91mEXredOgdO9pJ5oWT+n7kl//AAJcbU/IUNlhl7ErmPtHlcALJ5mES4ZSO6YsBMRERERhYjcRBmzf5GIw3Vne13PnYLf+Um/3WZHtQaVoncda/e43L6+xY6nSqxYkiUhre+nCTlM2ImIiIiIQkj3GVqivrLZZRRvsyq2ee+kCbPtMhcL+4i7xBMREREREVGvfVfd6bYM3pPGTgmHalg30FdM2ImIiIiIiKjXTreK3U4h2o7OYcJOREREREREvZYQLZZWirajc/iOERERERERUa+NTYtAokU5tYyPkDEmNVynHvUfTNiJiIiIiIio18wmCUumxyq2uW6YHWYTK7L7igk7ERERERER9cmUzCgUXRnfY6Y90WLCg3NjkZsoB6hnwY1l3ShgbHYZuysacKq5HYNjolAwfBBH3YiIiIiIgtSUzCjkj4jEd9WdON1qR0K0CWPTIiDJMqpLA9274MSEnQJi08E6PL7xMGqa2l2PpcZFYfn8bMzNSQ1gz4iIiIiIqLfMJgk5GZFuj9ltnF3vLS6JJ93tr5dwz//b75asA0BtUzuWvlaKkrKaAPWMiIiIiIjIOJiwk65sdhnvnTTB0xib87HHPiqHzc5ROCIiIiIiCm1M2ElXeytPo7HT+33qMoCapnbsrmjQr1NEREREREQGxISddHWquUOwXbt6IyIiIiIion6MCTvpanBMpHojAINjovzcEyIiIiIiImNjwk66unhoAuIjZHhbFC/BsVt8wfBBenaLiIiIiIjIcJiwk67MJgnXDbMDQI+k3fn35fOzWY+diIiIiIhCHhN20l1uooznfpWLlDj3Ze8pcVFYffNE1mEnIiIiIiICEBboDlBomjMuGfMuSsfuigacam7H4BjHMnjOrBMRERERETkwYaeAMZskTB2ZGOhuEBERERERGRKXxBMREREREREZEBN2IiIiIiIiIgNiwk5ERERERERkQEzYiYiIiIiIiAyICTsRERERERGRATFhJyIiIiIiIjKgkC7rJssyAMBqtboe6+rqQltbG6xWK8LDwxVfL9pWpJ1aG1/6ZWSBug5/nFeLY/bmGIxR/wvEtYRqjIq2Z4y66y8xqsVx+1OMWtusaGtrg9Qo4UzEGaFrMSq7zY62tjbUn66Hyazf/JA/ztvXY/bm9b6+RrS9Wru+Ph9MGKN9f70vr9MrRts7212fszgrfCm6c+afznxUSUgn7M3NzQCAIUOGBLgnREREREREFEqam5sRFxen2EaSRdL6fsput6O6uhoxMTGQJMn1eH5+Pvbs2SN0DNG2Iu2U2litVgwZMgQ//PADYmNjhfpmVL68v0Y/rxbH7M0xGKP+F4g4DdUYFW3PGHXXX2JUi+MyRo2Jv+/79np/xKhIO8ZocJ43EDHq6+sYo+fIsozm5makpaXBZFJenRDSM+wmkwkZGRk9HjebzcI/YNG2Iu1E2sTGxho6+ET48v4a/bxaHLM3x2CM+l8g4jRUY1S0PWPUXX+JUS2Oyxg1Jv6+79vr/RGjIu0Yo8F53kDEqK+vY4y6U5tZdzKvWLFihX+7EpwKCgo0byvSzlubjo4OrFy5Eg8//DAiIyOF+2ZUvry/Rj+vFsfszTEYo/4XiDgN1RgVbc8YdddfYlSL4zJGjYm/7/v2en/EqEg7xmhwnjcQMerr6xijvgvpJfHBxGq1Ii4uDk1NTYYfLaLQxBglo2OMktExRsnoGKNkdP0xRjnDHkTMZjNmzJiBsLCQvpOBDIwxSkbHGCWjY4yS0TFGyej6W4xyhp2IiIiIiIjIgIK7gCIRERERERFRP8WEnYiIiIiIiMiAmLATERERERERGRATdiIiIiIiIiIDYsJOREREREREZEBM2PuZxsZGXHzxxcjLy0NOTg7WrFkT6C4Rufnhhx8wY8YMZGdn46KLLsLbb78d6C4R9XDttdciISEB119/faC7QgQA+PjjjzF69GiMGjUKL730UqC7Q9QDPzfJ6IL1OyjLuvUzNpsNHR0dGDhwIFpbW5GTk4O9e/ciMTEx0F0jAgDU1NSgrq4OeXl5qK2txaRJk3DkyBFER0cHumtELlu2bEFzczPWrVuHd955J9DdoRB39uxZZGdnY/PmzYiLi8OkSZPw5Zdf8nc7GQo/N8nogvU7KGfY+xmz2YyBAwcCADo6OiDLMjgmQ0aSmpqKvLw8AEBKSgqSkpLQ0NAQ4F4RuZsxYwZiYmIC3Q0iAMDu3bsxbtw4pKenw2KxYN68efj3v/8d6G4RueHnJhldsH4HZcKus23btmH+/PlIS0uDJEl4//33e7RZtWoVhg0bhqioKEyePBm7d+/26RyNjY3Izc1FRkYGioqKkJSUpFX3KQToEaNOX3/9NWw2G4YMGdLXblMI0TNGibTQ15itrq5Genq66+/p6emoqqrSpe8UGvi5SsFAyzgNpu+gTNh11traitzcXKxatcrj8+vXr8f999+P5cuXo7S0FLm5uZgzZw5OnTrlauO8P737n+rqagBAfHw89u/fj4qKCrzxxhuoq6vT5dqof9AjRgGgoaEBt9xyC/75z3/6/Zqof9ErRom0okXMEvkTY5SCgVZxGnTfQWUKGADyv/71L7fHCgoK5GXLlrn+brPZ5LS0NPmJJ57o1TmWLl0qv/32233qJ4Uuf8Voe3u7XFhYKL/66qua9ZVCkz8/Rzdv3iwvXLhQk34SOfUmZnfs2CFfc801rud/+9vfyq+//ro+HaaQ05fPVX5ukl56G6fB+B2UM+wG0tnZia+//hozZ850PWYymTBz5kzs3LlT6Bh1dXVobm4GADQ1NWHbtm0YPXq0X/pLoUeLGJVlGbfddhuuuOIKLFq0yF9dpRClRYwS6UkkZgsKClBWVoaqqiq0tLRg48aNmDNnTqC6TCGGn6sUDETiNFi/gzJhN5CffvoJNpsNycnJbo8nJyejtrZW6BiVlZUoLCxEbm4uCgsLcc8992D8+PH+6C6FIC1idMeOHVi/fj3ef/995OXlIS8vDwcOHPBHdykEaRGjADBz5kzccMMN2LBhAzIyMvillPxGJGbDwsLw9NNP4/LLL0deXh4eeOAB7hBPuhH9XOXnJgWSSJwG63fQsEB3gLRVUFCAffv2BbobRF5NmzYNdrs90N0gUvSf//wn0F0gcrNgwQIsWLAg0N0g8oqfm2R0wfodlDPsBpKUlASz2dxjk7i6ujqkpKQEqFdE5zBGyegYoxRsGLNkdIxRCgb9OU6ZsBtIREQEJk2ahM8++8z1mN1ux2effYapU6cGsGdEDoxRMjrGKAUbxiwZHWOUgkF/jlMuiddZS0sLjh075vp7RUUF9u3bh0GDBuHCCy/E/fffj1tvvRUXX3wxCgoK8Pe//x2tra1YvHhxAHtNoYQxSkbHGKVgw5glo2OMUjAI2TgN9Db1oWbz5s0ygB5/br31Vleb5557Tr7wwgvliIgIuaCgQN61a1fgOkwhhzFKRscYpWDDmCWjY4xSMAjVOJVkWZb1GBggIiIiIiIiInG8h52IiIiIiIjIgJiwExERERERERkQE3YiIiIiIiIiA2LCTkRERERERGRATNiJiIiIiIiIDIgJOxEREREREZEBMWEnIiIiIiIiMiAm7EREREREREQGxISdiIjIYG677TZcc801upyrvr4egwcPxsmTJ3U5nz9t2bIFkiShsbFRtW15eTkyMjLQ2tqqQ8+IiIh6hwk7ERGRjiRJUvyzYsUKPPPMM3jllVd06c/jjz+Oq6++GsOGDQMAnDx5EpIkYd++fbqcv7snnngCZrMZTz75pM+vveSSS1BTU4O4uDjVttnZ2ZgyZQr+9re/9aabREREupBkWZYD3QkiIqJQUVtb6/r/9evX49FHH8Xhw4ddj1ksFlgsFl360tbWhtTUVGzatAlTpkwB4EjYhw8fjm+++QZ5eXm69ON8o0aNwvXXX4/3338f3333nV/P9cknn+DOO+/E999/j7CwML+ei4iIqDc4w05ERKSjlJQU15+4uDhIkuT2mMVi6bEkfsaMGbjnnntw3333ISEhAcnJyVizZg1aW1uxePFixMTEIDMzExs3bnQ7V1lZGebNmweLxYLk5GQsWrQIP/30k+v5DRs2IDIy0pWsi+jo6MC9996LwYMHIyoqCtOmTcOePXvc2nz44YcYNWoUoqKicPnll2PdunVCS9W3bt2KM2fO4E9/+hOsViu+/PJL13OyLGPmzJmYM2cOnHMNDQ0NyMjIwKOPPgqg55L4yspKzJ8/HwkJCYiOjsa4ceOwYcMG1zFnzZqFhoYGbN26Vfj6iYiI9MSEnYiIKAisW7cOSUlJ2L17N+655x4sXboUN9xwAy655BKUlpZi9uzZWLRoEdra2gAAjY2NuOKKKzBhwgTs3bsXJSUlqKurw4033ug65vbt2zFp0iSf+vH73/8e7777LtatW4fS0lJkZmZizpw5aGhoAABUVFTg+uuvxzXXXIP9+/fjrrvuwh//+EehY69duxY33XQTwsPDcdNNN2Ht2rWu5yRJwrp167Bnzx48++yzAIC7774b6enproS9u2XLlqGjowPbtm3DgQMH8Ne//tVt9UJERATy8vKwfft2n94DIiIivXD9FxERURDIzc3FI488AgB4+OGHsXLlSiQlJeHOO+8EADz66KNYvXo1vv32W0yZMgXPP/88JkyYgL/85S+uYxQXF2PIkCE4cuQIsrKyUFlZibS0NOE+tLa2YvXq1XjllVcwb948AMCaNWvw6aefYu3atSgqKsKLL76I0aNHu+5BHz16NMrKyvD4448rHttqteKdd97Bzp07AQA333wzCgsL8cwzz7iS7PT0dLz44ou45ZZbUFtbiw0bNuCbb77xupz9+++/x8KFCzF+/HgAwIgRI3q0SUtLQ2VlpfB7QEREpCfOsBMREQWBiy66yPX/ZrMZiYmJrkQUAJKTkwEAp06dAgDs378fmzdvdt0Tb7FYMGbMGADA8ePHAQBnzpxBVFSUcB+OHz+Orq4uXHrppa7HwsPDUVBQ4Lrf/PDhw8jPz3d7XUFBgeqx33zzTYwcORK5ubkAgLy8PAwdOhTr1693a3fDDTfg2muvxcqVK/HUU09h1KhRXo9577334s9//jMuvfRSLF++HN9++22PNgMGDHCtSiAiIjIaJuxERERBIDw83O3vkiS5PSZJEgDAbrcDAFpaWjB//nzs27fP7c/Ro0cxffp0AEBSUhJOnz6t0xUoW7t2LQ4ePIiwsDDXn/LychQXF7u1a2trw9dffw2z2YyjR48qHvOOO+7AiRMnsGjRIhw4cAAXX3wxnnvuObc2DQ0NuOCCCzS/HiIiIi0wYSciIuqHJk6ciIMHD2LYsGHIzMx0+xMdHQ0AmDBhAsrLy4WPOXLkSERERGDHjh2ux7q6urBnzx5kZ2cDcCyB37t3r9vrum9K192BAwewd+9ebNmyxW1wYcuWLdi5cycOHTrkavvAAw/AZDJh48aNePbZZ/H5558rHnvIkCG4++678d577+GBBx7AmjVr3J4vKyvDhAkThK6fiIhIb0zYiYiI+qFly5ahoaEBN910E/bs2YPjx49j06ZNWLx4MWw2GwBgzpw5OHjwoMdZ9sOHD/eYnY+IiMDSpUtRVFSEkpISlJeX484770RbWxtuv/12AMBdd92FQ4cO4aGHHsKRI0fw1ltvuWrKO1cBdLd27VoUFBRg+vTpyMnJcf2ZPn068vPzXZvPffLJJyguLsbrr7+OWbNmoaioCLfeeqvXVQL33XcfNm3ahIqKCpSWlmLz5s0YO3as6/mTJ0+iqqoKM2fO7PX7TERE5E9M2ImIiPqhtLQ07NixAzabDbNnz8b48eNx3333IT4+HiaT49f/+PHjMXHiRLz11ls9Xv+rX/0KEyZMcPtTV1eHlStXYuHChVi0aBEmTpyIY8eOYdOmTUhISAAADB8+HO+88w7ee+89XHTRRVi9erVrl/jIyMge5+ns7MRrr72GhQsXeryOhQsX4tVXX8V///tf3H777VixYgUmTpwIAHjssceQnJyMu+++2+NrbTYbli1bhrFjx2Lu3LnIysrCCy+84Hr+zTffxOzZszF06FAf3lkiIiL9SLKzmCkRERGFnE8++QRFRUUoKytzJfJae/zxx/GPf/wDP/zwg1+O3xudnZ0YNWoU3njjDbdN9IiIiIyEZd2IiIhC2FVXXYWjR4+iqqoKQ4YM0eSYL7zwAvLz85GYmIgdO3bgySefxG9+8xtNjq2V77//Hn/4wx+YrBMRkaFxhp2IiIg09bvf/Q7r169HQ0MDLrzwQixatAgPP/yw13rpRERE5BkTdiIiIiIiIiID4qZzRERERERERAbEhJ2IiIiIiIjIgJiwExERERERERkQE3YiIiIiIiIiA2LCTkRERERERGRATNiJiIiIiIiIDIgJOxEREREREZEBMWEnIiIiIiIiMiAm7EREREREREQG9P8BwkxUdM9fCBcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "65bnddpW1bKT",
        "outputId": "15fcd72f-3bda-482e-a635-73a75d820b4f"
      },
      "source": [
        "df[df['time']>1]['pressure'].mean()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2298.7667567567564"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zn6xqaDF1r4V"
      },
      "source": [
        "tmin_iarf = 1 #hr\n",
        "tmax_iarf = 50 #hrs"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0mOm11fTyfnC"
      },
      "source": [
        "# plt.style.use('default')\n",
        "# plt.figure(figsize=(12,4))\n",
        "\n",
        "# plt.scatter(np.log(df['time']), np.log(df['Pdash']),lw=3)\n",
        "# plt.scatter(np.log(df['time']), np.log(df['delP']),lw=3)\n",
        "\n",
        "# plt.xlabel('Log-Time') ; plt.ylabel('Log-Pressure')\n",
        "\n",
        "# plt.title('Log-Log Plot : Drawdown Testing')\n",
        "\n",
        "\n",
        "# plt.grid()"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dh8d3CEj2EMa"
      },
      "source": [
        "### D. Semi-Log Plot for IARF MTR only"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cf4CR734zgeG"
      },
      "source": [
        "df_semilog = df[(df['time']>=tmin_iarf) & (df['time'] <= tmax_iarf)]"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "ww6HIF3v2VEt",
        "outputId": "5c470990-3e5e-4792-9251-5dcd41cc922d"
      },
      "source": [
        "plt.style.use('default')\n",
        "plt.figure(figsize=(12,4))\n",
        "\n",
        "plt.semilogx(df['time'], df['pressure'],'x',color='green',label='All Time Domains')\n",
        "plt.semilogx(df_semilog['time'], df_semilog['pressure'],color='black',lw=3.5,label='Middle Time Region/IARF')\n",
        "\n",
        "plt.xlabel('Log-Time') ; plt.ylabel('Pressure')\n",
        "\n",
        "plt.title('Semilog Plot : Drawdown Testing')\n",
        "\n",
        "plt.legend()\n",
        "plt.grid()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rnYLUc_73Mmn"
      },
      "source": [
        "## CONCLUSION : Finding Permeability."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "UV6kQhHj2ell",
        "outputId": "3273150f-4a44-4019-8e69-7abb8ec285aa"
      },
      "source": [
        "t = np.log(df_semilog['time'])\n",
        "P = df_semilog['pressure']\n",
        "\n",
        "\n",
        "sns.regplot(t,P)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe206401b90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bRT-IVH44R_d",
        "outputId": "4dd748a3-1bd1-4197-cb88-3f8ad0da913e"
      },
      "source": [
        "#Polyfit\n",
        "np.polyfit(t,P,1)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ -17.7869321 , 2338.42912687])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49-B2aq-4j3Q"
      },
      "source": [
        "[slope,intercept]=np.polyfit(t,P,1)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0_LXGw7f4r8s",
        "outputId": "f85bbcf1-6c2c-4899-ff3e-77e5d8a9e4b7"
      },
      "source": [
        "abs(slope)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17.786932104989376"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7TBqys7K4xpL"
      },
      "source": [
        "m = abs(slope)"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LzxoS3q24xEt"
      },
      "source": [
        "## $ m = 162.6 \\frac {q \\mu B} {k h} $"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "deBSMwQq6Y_S",
        "outputId": "d6596bb9-9d22-4284-fdd9-e048e8c4af04"
      },
      "source": [
        "q = 125 #stb/d\n",
        "mu = 2.1 #cp\n",
        "B = 1.14 #rb/Stb\n",
        "h = 50 #ft\n",
        "\n",
        "k = 162.6*q*mu/m/h\n",
        "\n",
        "print(f'Formation Permeability is : {np.round(k,2)} miliDarcies')"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Formation Permeability is : 47.99 miliDarcies\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0o-dMKn4uJy"
      },
      "source": [
        "And that's how you calculate Permeability using a Well Test."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3nAPpVWr5GoC"
      },
      "source": [
        "# 2. Calculationg Reserves & Forecasts with Data : DCA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "L20hSpXr4tCh",
        "outputId": "1d12c248-cd14-40a3-b221-897b0b516a15"
      },
      "source": [
        "dca_df = pd.read_csv('https://raw.githubusercontent.com/yohanesnuwara/pyreservoir/master/data/norne_production_rate_sample.csv',\n",
        "                     index_col = 0 , parse_dates = True)\n",
        "\n",
        "dca_df.head()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rate (SCF/d)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2004-04-01</th>\n",
              "      <td>2706039.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-05-01</th>\n",
              "      <td>2492086.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-06-02</th>\n",
              "      <td>1816846.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-07-02</th>\n",
              "      <td>1920207.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-07-04</th>\n",
              "      <td>1683521.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Rate (SCF/d)\n",
              "Date                    \n",
              "2004-04-01     2706039.0\n",
              "2004-05-01     2492086.2\n",
              "2004-06-02     1816846.1\n",
              "2004-07-02     1920207.4\n",
              "2004-07-04     1683521.4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "W5d3y2GV7orG",
        "outputId": "5aaec8eb-e893-4b3d-b657-b2e8f06e04ff"
      },
      "source": [
        "dca_df.plot(figsize=(12,4))\n",
        "plt.grid()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "4jh3l34K7qZ0",
        "outputId": "fd6a8e82-c27b-46fe-84d0-5c38ba8a4b60"
      },
      "source": [
        "plt.scatter(y=dca_df['Rate (SCF/d)'],x=dca_df.index,marker='*')\n",
        "\n",
        "# plt.ylabel('Rate (SCF/d)')\n",
        "plt.grid()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S1lt4VwW7tRw"
      },
      "source": [
        "def day_maker(df):\n",
        "    \n",
        "    '''\n",
        "    Pass a Time-Series DataFrame to it and it will\n",
        "    return a days column. Subtracts dates and makes days. \n",
        "    \n",
        "    Returned is a days (np array).\n",
        "    '''\n",
        "    \n",
        "    days = []\n",
        "\n",
        "    for d in range(len(df)):\n",
        "    \n",
        "        delta = df.index[d] - df.index[0]\n",
        "    \n",
        "        days.append(delta.days)\n",
        "    \n",
        "    days = np.array(days)\n",
        "    \n",
        "    return days"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACq6tlz97yCs"
      },
      "source": [
        "dca_df['days'] = day_maker(dca_df)"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "8wFbAXEr72hU",
        "outputId": "f775d9a2-086a-44aa-c4a3-ef452f196127"
      },
      "source": [
        "dca_df.head()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rate (SCF/d)</th>\n",
              "      <th>days</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2004-04-01</th>\n",
              "      <td>2706039.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-05-01</th>\n",
              "      <td>2492086.2</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-06-02</th>\n",
              "      <td>1816846.1</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-07-02</th>\n",
              "      <td>1920207.4</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004-07-04</th>\n",
              "      <td>1683521.4</td>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Rate (SCF/d)  days\n",
              "Date                          \n",
              "2004-04-01     2706039.0     0\n",
              "2004-05-01     2492086.2    30\n",
              "2004-06-02     1816846.1    62\n",
              "2004-07-02     1920207.4    92\n",
              "2004-07-04     1683521.4    94"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "IrovcYVK74mW",
        "outputId": "dd32f874-3862-4ebd-b183-f45b02d58251"
      },
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "plt.scatter(y=dca_df['Rate (SCF/d)'],x=dca_df['days'],marker='v',color='green')\n",
        "\n",
        "# plt.ylabel('Rate (SCF/d)')\n",
        "plt.grid()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5tEyCoj76eQ"
      },
      "source": [
        "from scipy.optimize import curve_fit"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7e6ukkxK78Vo"
      },
      "source": [
        "def q_hyp(t,qi,b,d):\n",
        "    \n",
        "    qfit = qi/(np.abs((1 + b * d* t))**(1/b))\n",
        "    \n",
        "    return qfit\n",
        "\n",
        "\n",
        "def hyp_fitter(q,t):\n",
        "        \n",
        "    #First we have to Normalize so that it converges well and quick.\n",
        "    q_n = q/max(q)\n",
        "    t_n = t/max(t)\n",
        "    \n",
        "    #curve-fit (optimization of parameters)\n",
        "    params = curve_fit(q_hyp,t_n,q_n)\n",
        "    [qi,b,d] = params[0]\n",
        "    \n",
        "    #These are for normalized t and q. \n",
        "    #We must re-adjust for q and t (non-normalized)\n",
        "    d_f = d/max(t)\n",
        "    qi_f = qi*max(q)\n",
        "    \n",
        "    #Now we can use these parameters. \n",
        "    q_hyp_fit = q_hyp(t,qi_f,b,d_f)\n",
        "    \n",
        "    return q_hyp_fit,params"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q7HjmrjT7-fO"
      },
      "source": [
        "q = dca_df['Rate (SCF/d)'] ; t = dca_df['days']\n",
        "q_fit ,params = hyp_fitter(q,t)"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "TFirhocn8AL-",
        "outputId": "394631a7-7536-40ba-fa3e-77679ca37c95"
      },
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "plt.scatter(t,q,marker='v',color='green',label='Actual Production Data')\n",
        "plt.plot(t,q_fit,color='brown',lw=4,label='DCA Model Fit')\n",
        "\n",
        "plt.ylabel(df.columns[0])\n",
        "\n",
        "plt.grid()\n",
        "plt.legend()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fe206263810>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_20ELjy7LOib"
      },
      "source": [
        "Reserves calculation : Numerical Integration : Trapezoidal Rule"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cBpESC89KoWM",
        "outputId": "53e902ad-af5d-492b-fd7f-ba6d6c36b380"
      },
      "source": [
        "q"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2004-04-01    2706039.00\n",
              "2004-05-01    2492086.20\n",
              "2004-06-02    1816846.10\n",
              "2004-07-02    1920207.40\n",
              "2004-07-04    1683521.40\n",
              "                 ...    \n",
              "2006-11-01     289382.91\n",
              "2006-11-09     293290.66\n",
              "2006-11-11     293359.31\n",
              "2006-11-17     351398.16\n",
              "2006-12-01     351398.16\n",
              "Name: Rate (SCF/d), Length: 64, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2QWqZcr6Kp7l",
        "outputId": "06c6b96c-59a0-4c68-b9b9-bd1fc8bdaee3"
      },
      "source": [
        "q_fit"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2004-04-01    2.866266e+06\n",
              "2004-05-01    2.367275e+06\n",
              "2004-06-02    1.966120e+06\n",
              "2004-07-02    1.675941e+06\n",
              "2004-07-04    1.658923e+06\n",
              "                  ...     \n",
              "2006-11-01    1.658027e+05\n",
              "2006-11-09    1.636889e+05\n",
              "2006-11-11    1.631667e+05\n",
              "2006-11-17    1.616152e+05\n",
              "2006-12-01    1.580799e+05\n",
              "Name: days, Length: 64, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ReS65LFSKhQF"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error as mse"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lae_QS18KgbK"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i-WI_ixj8E9g"
      },
      "source": [
        "Notice that in this DCA technique (Physics base, Arps) we MUST know the equation prior to the project. We need that info otherwise we cannot do anything.\n",
        "Whereas in Data Driven approaches, we normally start with ONLY DATA and fit the model that suits the best, use this model for Production Forecasting."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qeR_eykA8DvF"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}